Actual source code: aij.c
1: /*
2: Defines the basic matrix operations for the AIJ (compressed row)
3: matrix storage format.
4: */
6: #include <../src/mat/impls/aij/seq/aij.h>
7: #include <petscblaslapack.h>
8: #include <petscbt.h>
9: #include <petsc/private/kernels/blocktranspose.h>
11: /* defines MatSetValues_Seq_Hash(), MatAssemblyEnd_Seq_Hash(), MatSetUp_Seq_Hash() */
12: #define TYPE AIJ
13: #define TYPE_BS
14: #include "../src/mat/impls/aij/seq/seqhashmatsetvalues.h"
15: #include "../src/mat/impls/aij/seq/seqhashmat.h"
16: #undef TYPE
17: #undef TYPE_BS
19: static PetscErrorCode MatSeqAIJSetTypeFromOptions(Mat A)
20: {
21: PetscBool flg;
22: char type[256];
24: PetscFunctionBegin;
25: PetscObjectOptionsBegin((PetscObject)A);
26: PetscCall(PetscOptionsFList("-mat_seqaij_type", "Matrix SeqAIJ type", "MatSeqAIJSetType", MatSeqAIJList, "seqaij", type, 256, &flg));
27: if (flg) PetscCall(MatSeqAIJSetType(A, type));
28: PetscOptionsEnd();
29: PetscFunctionReturn(PETSC_SUCCESS);
30: }
32: static PetscErrorCode MatGetColumnReductions_SeqAIJ(Mat A, PetscInt type, PetscReal *reductions)
33: {
34: PetscInt i, m, n;
35: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
37: PetscFunctionBegin;
38: PetscCall(MatGetSize(A, &m, &n));
39: PetscCall(PetscArrayzero(reductions, n));
40: if (type == NORM_2) {
41: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i] * aij->a[i]);
42: } else if (type == NORM_1) {
43: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscAbsScalar(aij->a[i]);
44: } else if (type == NORM_INFINITY) {
45: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] = PetscMax(PetscAbsScalar(aij->a[i]), reductions[aij->j[i]]);
46: } else if (type == REDUCTION_SUM_REALPART || type == REDUCTION_MEAN_REALPART) {
47: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscRealPart(aij->a[i]);
48: } else if (type == REDUCTION_SUM_IMAGINARYPART || type == REDUCTION_MEAN_IMAGINARYPART) {
49: for (i = 0; i < aij->i[m]; i++) reductions[aij->j[i]] += PetscImaginaryPart(aij->a[i]);
50: } else SETERRQ(PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONG, "Unknown reduction type");
52: if (type == NORM_2) {
53: for (i = 0; i < n; i++) reductions[i] = PetscSqrtReal(reductions[i]);
54: } else if (type == REDUCTION_MEAN_REALPART || type == REDUCTION_MEAN_IMAGINARYPART) {
55: for (i = 0; i < n; i++) reductions[i] /= m;
56: }
57: PetscFunctionReturn(PETSC_SUCCESS);
58: }
60: static PetscErrorCode MatFindOffBlockDiagonalEntries_SeqAIJ(Mat A, IS *is)
61: {
62: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
63: PetscInt i, m = A->rmap->n, cnt = 0, bs = A->rmap->bs;
64: const PetscInt *jj = a->j, *ii = a->i;
65: PetscInt *rows;
67: PetscFunctionBegin;
68: for (i = 0; i < m; i++) {
69: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) cnt++;
70: }
71: PetscCall(PetscMalloc1(cnt, &rows));
72: cnt = 0;
73: for (i = 0; i < m; i++) {
74: if ((ii[i] != ii[i + 1]) && ((jj[ii[i]] < bs * (i / bs)) || (jj[ii[i + 1] - 1] > bs * ((i + bs) / bs) - 1))) {
75: rows[cnt] = i;
76: cnt++;
77: }
78: }
79: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, is));
80: PetscFunctionReturn(PETSC_SUCCESS);
81: }
83: PetscErrorCode MatFindZeroDiagonals_SeqAIJ_Private(Mat A, PetscInt *nrows, PetscInt **zrows)
84: {
85: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
86: const MatScalar *aa;
87: PetscInt i, m = A->rmap->n, cnt = 0;
88: const PetscInt *ii = a->i, *jj = a->j, *diag;
89: PetscInt *rows;
91: PetscFunctionBegin;
92: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
93: PetscCall(MatMarkDiagonal_SeqAIJ(A));
94: diag = a->diag;
95: for (i = 0; i < m; i++) {
96: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) cnt++;
97: }
98: PetscCall(PetscMalloc1(cnt, &rows));
99: cnt = 0;
100: for (i = 0; i < m; i++) {
101: if ((diag[i] >= ii[i + 1]) || (jj[diag[i]] != i) || (aa[diag[i]] == 0.0)) rows[cnt++] = i;
102: }
103: *nrows = cnt;
104: *zrows = rows;
105: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
106: PetscFunctionReturn(PETSC_SUCCESS);
107: }
109: static PetscErrorCode MatFindZeroDiagonals_SeqAIJ(Mat A, IS *zrows)
110: {
111: PetscInt nrows, *rows;
113: PetscFunctionBegin;
114: *zrows = NULL;
115: PetscCall(MatFindZeroDiagonals_SeqAIJ_Private(A, &nrows, &rows));
116: PetscCall(ISCreateGeneral(PetscObjectComm((PetscObject)A), nrows, rows, PETSC_OWN_POINTER, zrows));
117: PetscFunctionReturn(PETSC_SUCCESS);
118: }
120: static PetscErrorCode MatFindNonzeroRows_SeqAIJ(Mat A, IS *keptrows)
121: {
122: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
123: const MatScalar *aa;
124: PetscInt m = A->rmap->n, cnt = 0;
125: const PetscInt *ii;
126: PetscInt n, i, j, *rows;
128: PetscFunctionBegin;
129: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
130: *keptrows = NULL;
131: ii = a->i;
132: for (i = 0; i < m; i++) {
133: n = ii[i + 1] - ii[i];
134: if (!n) {
135: cnt++;
136: goto ok1;
137: }
138: for (j = ii[i]; j < ii[i + 1]; j++) {
139: if (aa[j] != 0.0) goto ok1;
140: }
141: cnt++;
142: ok1:;
143: }
144: if (!cnt) {
145: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
146: PetscFunctionReturn(PETSC_SUCCESS);
147: }
148: PetscCall(PetscMalloc1(A->rmap->n - cnt, &rows));
149: cnt = 0;
150: for (i = 0; i < m; i++) {
151: n = ii[i + 1] - ii[i];
152: if (!n) continue;
153: for (j = ii[i]; j < ii[i + 1]; j++) {
154: if (aa[j] != 0.0) {
155: rows[cnt++] = i;
156: break;
157: }
158: }
159: }
160: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
161: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, cnt, rows, PETSC_OWN_POINTER, keptrows));
162: PetscFunctionReturn(PETSC_SUCCESS);
163: }
165: PetscErrorCode MatDiagonalSet_SeqAIJ(Mat Y, Vec D, InsertMode is)
166: {
167: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)Y->data;
168: PetscInt i, m = Y->rmap->n;
169: const PetscInt *diag;
170: MatScalar *aa;
171: const PetscScalar *v;
172: PetscBool missing;
174: PetscFunctionBegin;
175: if (Y->assembled) {
176: PetscCall(MatMissingDiagonal_SeqAIJ(Y, &missing, NULL));
177: if (!missing) {
178: diag = aij->diag;
179: PetscCall(VecGetArrayRead(D, &v));
180: PetscCall(MatSeqAIJGetArray(Y, &aa));
181: if (is == INSERT_VALUES) {
182: for (i = 0; i < m; i++) aa[diag[i]] = v[i];
183: } else {
184: for (i = 0; i < m; i++) aa[diag[i]] += v[i];
185: }
186: PetscCall(MatSeqAIJRestoreArray(Y, &aa));
187: PetscCall(VecRestoreArrayRead(D, &v));
188: PetscFunctionReturn(PETSC_SUCCESS);
189: }
190: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
191: }
192: PetscCall(MatDiagonalSet_Default(Y, D, is));
193: PetscFunctionReturn(PETSC_SUCCESS);
194: }
196: PetscErrorCode MatGetRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *m, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
197: {
198: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
199: PetscInt i, ishift;
201: PetscFunctionBegin;
202: if (m) *m = A->rmap->n;
203: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
204: ishift = 0;
205: if (symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) {
206: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, ishift, oshift, (PetscInt **)ia, (PetscInt **)ja));
207: } else if (oshift == 1) {
208: PetscInt *tia;
209: PetscInt nz = a->i[A->rmap->n];
210: /* malloc space and add 1 to i and j indices */
211: PetscCall(PetscMalloc1(A->rmap->n + 1, &tia));
212: for (i = 0; i < A->rmap->n + 1; i++) tia[i] = a->i[i] + 1;
213: *ia = tia;
214: if (ja) {
215: PetscInt *tja;
216: PetscCall(PetscMalloc1(nz + 1, &tja));
217: for (i = 0; i < nz; i++) tja[i] = a->j[i] + 1;
218: *ja = tja;
219: }
220: } else {
221: *ia = a->i;
222: if (ja) *ja = a->j;
223: }
224: PetscFunctionReturn(PETSC_SUCCESS);
225: }
227: PetscErrorCode MatRestoreRowIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
228: {
229: PetscFunctionBegin;
230: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
231: if ((symmetric && A->structurally_symmetric != PETSC_BOOL3_TRUE) || oshift == 1) {
232: PetscCall(PetscFree(*ia));
233: if (ja) PetscCall(PetscFree(*ja));
234: }
235: PetscFunctionReturn(PETSC_SUCCESS);
236: }
238: PetscErrorCode MatGetColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
239: {
240: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
241: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
242: PetscInt nz = a->i[m], row, *jj, mr, col;
244: PetscFunctionBegin;
245: *nn = n;
246: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
247: if (symmetric) {
248: PetscCall(MatToSymmetricIJ_SeqAIJ(A->rmap->n, a->i, a->j, PETSC_TRUE, 0, oshift, (PetscInt **)ia, (PetscInt **)ja));
249: } else {
250: PetscCall(PetscCalloc1(n, &collengths));
251: PetscCall(PetscMalloc1(n + 1, &cia));
252: PetscCall(PetscMalloc1(nz, &cja));
253: jj = a->j;
254: for (i = 0; i < nz; i++) collengths[jj[i]]++;
255: cia[0] = oshift;
256: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
257: PetscCall(PetscArrayzero(collengths, n));
258: jj = a->j;
259: for (row = 0; row < m; row++) {
260: mr = a->i[row + 1] - a->i[row];
261: for (i = 0; i < mr; i++) {
262: col = *jj++;
264: cja[cia[col] + collengths[col]++ - oshift] = row + oshift;
265: }
266: }
267: PetscCall(PetscFree(collengths));
268: *ia = cia;
269: *ja = cja;
270: }
271: PetscFunctionReturn(PETSC_SUCCESS);
272: }
274: PetscErrorCode MatRestoreColumnIJ_SeqAIJ(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscBool *done)
275: {
276: PetscFunctionBegin;
277: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
279: PetscCall(PetscFree(*ia));
280: PetscCall(PetscFree(*ja));
281: PetscFunctionReturn(PETSC_SUCCESS);
282: }
284: /*
285: MatGetColumnIJ_SeqAIJ_Color() and MatRestoreColumnIJ_SeqAIJ_Color() are customized from
286: MatGetColumnIJ_SeqAIJ() and MatRestoreColumnIJ_SeqAIJ() by adding an output
287: spidx[], index of a->a, to be used in MatTransposeColoringCreate_SeqAIJ() and MatFDColoringCreate_SeqXAIJ()
288: */
289: PetscErrorCode MatGetColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *nn, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
290: {
291: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
292: PetscInt i, *collengths, *cia, *cja, n = A->cmap->n, m = A->rmap->n;
293: PetscInt nz = a->i[m], row, mr, col, tmp;
294: PetscInt *cspidx;
295: const PetscInt *jj;
297: PetscFunctionBegin;
298: *nn = n;
299: if (!ia) PetscFunctionReturn(PETSC_SUCCESS);
301: PetscCall(PetscCalloc1(n, &collengths));
302: PetscCall(PetscMalloc1(n + 1, &cia));
303: PetscCall(PetscMalloc1(nz, &cja));
304: PetscCall(PetscMalloc1(nz, &cspidx));
305: jj = a->j;
306: for (i = 0; i < nz; i++) collengths[jj[i]]++;
307: cia[0] = oshift;
308: for (i = 0; i < n; i++) cia[i + 1] = cia[i] + collengths[i];
309: PetscCall(PetscArrayzero(collengths, n));
310: jj = a->j;
311: for (row = 0; row < m; row++) {
312: mr = a->i[row + 1] - a->i[row];
313: for (i = 0; i < mr; i++) {
314: col = *jj++;
315: tmp = cia[col] + collengths[col]++ - oshift;
316: cspidx[tmp] = a->i[row] + i; /* index of a->j */
317: cja[tmp] = row + oshift;
318: }
319: }
320: PetscCall(PetscFree(collengths));
321: *ia = cia;
322: *ja = cja;
323: *spidx = cspidx;
324: PetscFunctionReturn(PETSC_SUCCESS);
325: }
327: PetscErrorCode MatRestoreColumnIJ_SeqAIJ_Color(Mat A, PetscInt oshift, PetscBool symmetric, PetscBool inodecompressed, PetscInt *n, const PetscInt *ia[], const PetscInt *ja[], PetscInt *spidx[], PetscBool *done)
328: {
329: PetscFunctionBegin;
330: PetscCall(MatRestoreColumnIJ_SeqAIJ(A, oshift, symmetric, inodecompressed, n, ia, ja, done));
331: PetscCall(PetscFree(*spidx));
332: PetscFunctionReturn(PETSC_SUCCESS);
333: }
335: static PetscErrorCode MatSetValuesRow_SeqAIJ(Mat A, PetscInt row, const PetscScalar v[])
336: {
337: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
338: PetscInt *ai = a->i;
339: PetscScalar *aa;
341: PetscFunctionBegin;
342: PetscCall(MatSeqAIJGetArray(A, &aa));
343: PetscCall(PetscArraycpy(aa + ai[row], v, ai[row + 1] - ai[row]));
344: PetscCall(MatSeqAIJRestoreArray(A, &aa));
345: PetscFunctionReturn(PETSC_SUCCESS);
346: }
348: /*
349: MatSeqAIJSetValuesLocalFast - An optimized version of MatSetValuesLocal() for SeqAIJ matrices with several assumptions
351: - a single row of values is set with each call
352: - no row or column indices are negative or (in error) larger than the number of rows or columns
353: - the values are always added to the matrix, not set
354: - no new locations are introduced in the nonzero structure of the matrix
356: This does NOT assume the global column indices are sorted
358: */
360: #include <petsc/private/isimpl.h>
361: PetscErrorCode MatSeqAIJSetValuesLocalFast(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
362: {
363: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
364: PetscInt low, high, t, row, nrow, i, col, l;
365: const PetscInt *rp, *ai = a->i, *ailen = a->ilen, *aj = a->j;
366: PetscInt lastcol = -1;
367: MatScalar *ap, value, *aa;
368: const PetscInt *ridx = A->rmap->mapping->indices, *cidx = A->cmap->mapping->indices;
370: PetscFunctionBegin;
371: PetscCall(MatSeqAIJGetArray(A, &aa));
372: row = ridx[im[0]];
373: rp = aj + ai[row];
374: ap = aa + ai[row];
375: nrow = ailen[row];
376: low = 0;
377: high = nrow;
378: for (l = 0; l < n; l++) { /* loop over added columns */
379: col = cidx[in[l]];
380: value = v[l];
382: if (col <= lastcol) low = 0;
383: else high = nrow;
384: lastcol = col;
385: while (high - low > 5) {
386: t = (low + high) / 2;
387: if (rp[t] > col) high = t;
388: else low = t;
389: }
390: for (i = low; i < high; i++) {
391: if (rp[i] == col) {
392: ap[i] += value;
393: low = i + 1;
394: break;
395: }
396: }
397: }
398: PetscCall(MatSeqAIJRestoreArray(A, &aa));
399: return PETSC_SUCCESS;
400: }
402: PetscErrorCode MatSetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
403: {
404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
405: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
406: PetscInt *imax = a->imax, *ai = a->i, *ailen = a->ilen;
407: PetscInt *aj = a->j, nonew = a->nonew, lastcol = -1;
408: MatScalar *ap = NULL, value = 0.0, *aa;
409: PetscBool ignorezeroentries = a->ignorezeroentries;
410: PetscBool roworiented = a->roworiented;
412: PetscFunctionBegin;
413: PetscCall(MatSeqAIJGetArray(A, &aa));
414: for (k = 0; k < m; k++) { /* loop over added rows */
415: row = im[k];
416: if (row < 0) continue;
417: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
418: rp = PetscSafePointerPlusOffset(aj, ai[row]);
419: if (!A->structure_only) ap = PetscSafePointerPlusOffset(aa, ai[row]);
420: rmax = imax[row];
421: nrow = ailen[row];
422: low = 0;
423: high = nrow;
424: for (l = 0; l < n; l++) { /* loop over added columns */
425: if (in[l] < 0) continue;
426: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
427: col = in[l];
428: if (v && !A->structure_only) value = roworiented ? v[l + k * n] : v[k + l * m];
429: if (!A->structure_only && value == 0.0 && ignorezeroentries && is == ADD_VALUES && row != col) continue;
431: if (col <= lastcol) low = 0;
432: else high = nrow;
433: lastcol = col;
434: while (high - low > 5) {
435: t = (low + high) / 2;
436: if (rp[t] > col) high = t;
437: else low = t;
438: }
439: for (i = low; i < high; i++) {
440: if (rp[i] > col) break;
441: if (rp[i] == col) {
442: if (!A->structure_only) {
443: if (is == ADD_VALUES) {
444: ap[i] += value;
445: (void)PetscLogFlops(1.0);
446: } else ap[i] = value;
447: }
448: low = i + 1;
449: goto noinsert;
450: }
451: }
452: if (value == 0.0 && ignorezeroentries && row != col) goto noinsert;
453: if (nonew == 1) goto noinsert;
454: PetscCheck(nonew != -1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero at (%" PetscInt_FMT ",%" PetscInt_FMT ") in the matrix", row, col);
455: if (A->structure_only) {
456: MatSeqXAIJReallocateAIJ_structure_only(A, A->rmap->n, 1, nrow, row, col, rmax, ai, aj, rp, imax, nonew, MatScalar);
457: } else {
458: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
459: }
460: N = nrow++ - 1;
461: a->nz++;
462: high++;
463: /* shift up all the later entries in this row */
464: PetscCall(PetscArraymove(rp + i + 1, rp + i, N - i + 1));
465: rp[i] = col;
466: if (!A->structure_only) {
467: PetscCall(PetscArraymove(ap + i + 1, ap + i, N - i + 1));
468: ap[i] = value;
469: }
470: low = i + 1;
471: A->nonzerostate++;
472: noinsert:;
473: }
474: ailen[row] = nrow;
475: }
476: PetscCall(MatSeqAIJRestoreArray(A, &aa));
477: PetscFunctionReturn(PETSC_SUCCESS);
478: }
480: static PetscErrorCode MatSetValues_SeqAIJ_SortedFullNoPreallocation(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
481: {
482: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
483: PetscInt *rp, k, row;
484: PetscInt *ai = a->i;
485: PetscInt *aj = a->j;
486: MatScalar *aa, *ap;
488: PetscFunctionBegin;
489: PetscCheck(!A->was_assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot call on assembled matrix.");
490: PetscCheck(m * n + a->nz <= a->maxnz, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Number of entries in matrix will be larger than maximum nonzeros allocated for %" PetscInt_FMT " in MatSeqAIJSetTotalPreallocation()", a->maxnz);
492: PetscCall(MatSeqAIJGetArray(A, &aa));
493: for (k = 0; k < m; k++) { /* loop over added rows */
494: row = im[k];
495: rp = aj + ai[row];
496: ap = PetscSafePointerPlusOffset(aa, ai[row]);
498: PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
499: if (!A->structure_only) {
500: if (v) {
501: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
502: v += n;
503: } else {
504: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
505: }
506: }
507: a->ilen[row] = n;
508: a->imax[row] = n;
509: a->i[row + 1] = a->i[row] + n;
510: a->nz += n;
511: }
512: PetscCall(MatSeqAIJRestoreArray(A, &aa));
513: PetscFunctionReturn(PETSC_SUCCESS);
514: }
516: /*@
517: MatSeqAIJSetTotalPreallocation - Sets an upper bound on the total number of expected nonzeros in the matrix.
519: Input Parameters:
520: + A - the `MATSEQAIJ` matrix
521: - nztotal - bound on the number of nonzeros
523: Level: advanced
525: Notes:
526: This can be called if you will be provided the matrix row by row (from row zero) with sorted column indices for each row.
527: Simply call `MatSetValues()` after this call to provide the matrix entries in the usual manner. This matrix may be used
528: as always with multiple matrix assemblies.
530: .seealso: [](ch_matrices), `Mat`, `MatSetOption()`, `MAT_SORTED_FULL`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`
531: @*/
532: PetscErrorCode MatSeqAIJSetTotalPreallocation(Mat A, PetscInt nztotal)
533: {
534: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
536: PetscFunctionBegin;
537: PetscCall(PetscLayoutSetUp(A->rmap));
538: PetscCall(PetscLayoutSetUp(A->cmap));
539: a->maxnz = nztotal;
540: if (!a->imax) { PetscCall(PetscMalloc1(A->rmap->n, &a->imax)); }
541: if (!a->ilen) {
542: PetscCall(PetscMalloc1(A->rmap->n, &a->ilen));
543: } else {
544: PetscCall(PetscMemzero(a->ilen, A->rmap->n * sizeof(PetscInt)));
545: }
547: /* allocate the matrix space */
548: if (A->structure_only) {
549: PetscCall(PetscMalloc1(nztotal, &a->j));
550: PetscCall(PetscMalloc1(A->rmap->n + 1, &a->i));
551: } else {
552: PetscCall(PetscMalloc3(nztotal, &a->a, nztotal, &a->j, A->rmap->n + 1, &a->i));
553: }
554: a->i[0] = 0;
555: if (A->structure_only) {
556: a->singlemalloc = PETSC_FALSE;
557: a->free_a = PETSC_FALSE;
558: } else {
559: a->singlemalloc = PETSC_TRUE;
560: a->free_a = PETSC_TRUE;
561: }
562: a->free_ij = PETSC_TRUE;
563: A->ops->setvalues = MatSetValues_SeqAIJ_SortedFullNoPreallocation;
564: A->preallocated = PETSC_TRUE;
565: PetscFunctionReturn(PETSC_SUCCESS);
566: }
568: static PetscErrorCode MatSetValues_SeqAIJ_SortedFull(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], const PetscScalar v[], InsertMode is)
569: {
570: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
571: PetscInt *rp, k, row;
572: PetscInt *ai = a->i, *ailen = a->ilen;
573: PetscInt *aj = a->j;
574: MatScalar *aa, *ap;
576: PetscFunctionBegin;
577: PetscCall(MatSeqAIJGetArray(A, &aa));
578: for (k = 0; k < m; k++) { /* loop over added rows */
579: row = im[k];
580: PetscCheck(n <= a->imax[row], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "Preallocation for row %" PetscInt_FMT " does not match number of columns provided", n);
581: rp = aj + ai[row];
582: ap = aa + ai[row];
583: if (!A->was_assembled) PetscCall(PetscMemcpy(rp, in, n * sizeof(PetscInt)));
584: if (!A->structure_only) {
585: if (v) {
586: PetscCall(PetscMemcpy(ap, v, n * sizeof(PetscScalar)));
587: v += n;
588: } else {
589: PetscCall(PetscMemzero(ap, n * sizeof(PetscScalar)));
590: }
591: }
592: ailen[row] = n;
593: a->nz += n;
594: }
595: PetscCall(MatSeqAIJRestoreArray(A, &aa));
596: PetscFunctionReturn(PETSC_SUCCESS);
597: }
599: static PetscErrorCode MatGetValues_SeqAIJ(Mat A, PetscInt m, const PetscInt im[], PetscInt n, const PetscInt in[], PetscScalar v[])
600: {
601: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
602: PetscInt *rp, k, low, high, t, row, nrow, i, col, l, *aj = a->j;
603: PetscInt *ai = a->i, *ailen = a->ilen;
604: const MatScalar *ap, *aa;
606: PetscFunctionBegin;
607: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
608: for (k = 0; k < m; k++) { /* loop over rows */
609: row = im[k];
610: if (row < 0) {
611: v += n;
612: continue;
613: } /* negative row */
614: PetscCheck(row < A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Row too large: row %" PetscInt_FMT " max %" PetscInt_FMT, row, A->rmap->n - 1);
615: rp = PetscSafePointerPlusOffset(aj, ai[row]);
616: ap = PetscSafePointerPlusOffset(aa, ai[row]);
617: nrow = ailen[row];
618: for (l = 0; l < n; l++) { /* loop over columns */
619: if (in[l] < 0) {
620: v++;
621: continue;
622: } /* negative column */
623: PetscCheck(in[l] < A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column too large: col %" PetscInt_FMT " max %" PetscInt_FMT, in[l], A->cmap->n - 1);
624: col = in[l];
625: high = nrow;
626: low = 0; /* assume unsorted */
627: while (high - low > 5) {
628: t = (low + high) / 2;
629: if (rp[t] > col) high = t;
630: else low = t;
631: }
632: for (i = low; i < high; i++) {
633: if (rp[i] > col) break;
634: if (rp[i] == col) {
635: *v++ = ap[i];
636: goto finished;
637: }
638: }
639: *v++ = 0.0;
640: finished:;
641: }
642: }
643: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
644: PetscFunctionReturn(PETSC_SUCCESS);
645: }
647: static PetscErrorCode MatView_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
648: {
649: Mat_SeqAIJ *A = (Mat_SeqAIJ *)mat->data;
650: const PetscScalar *av;
651: PetscInt header[4], M, N, m, nz, i;
652: PetscInt *rowlens;
654: PetscFunctionBegin;
655: PetscCall(PetscViewerSetUp(viewer));
657: M = mat->rmap->N;
658: N = mat->cmap->N;
659: m = mat->rmap->n;
660: nz = A->nz;
662: /* write matrix header */
663: header[0] = MAT_FILE_CLASSID;
664: header[1] = M;
665: header[2] = N;
666: header[3] = nz;
667: PetscCall(PetscViewerBinaryWrite(viewer, header, 4, PETSC_INT));
669: /* fill in and store row lengths */
670: PetscCall(PetscMalloc1(m, &rowlens));
671: for (i = 0; i < m; i++) rowlens[i] = A->i[i + 1] - A->i[i];
672: PetscCall(PetscViewerBinaryWrite(viewer, rowlens, m, PETSC_INT));
673: PetscCall(PetscFree(rowlens));
674: /* store column indices */
675: PetscCall(PetscViewerBinaryWrite(viewer, A->j, nz, PETSC_INT));
676: /* store nonzero values */
677: PetscCall(MatSeqAIJGetArrayRead(mat, &av));
678: PetscCall(PetscViewerBinaryWrite(viewer, av, nz, PETSC_SCALAR));
679: PetscCall(MatSeqAIJRestoreArrayRead(mat, &av));
681: /* write block size option to the viewer's .info file */
682: PetscCall(MatView_Binary_BlockSizes(mat, viewer));
683: PetscFunctionReturn(PETSC_SUCCESS);
684: }
686: static PetscErrorCode MatView_SeqAIJ_ASCII_structonly(Mat A, PetscViewer viewer)
687: {
688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
689: PetscInt i, k, m = A->rmap->N;
691: PetscFunctionBegin;
692: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
693: for (i = 0; i < m; i++) {
694: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
695: for (k = a->i[i]; k < a->i[i + 1]; k++) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ") ", a->j[k]));
696: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
697: }
698: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
699: PetscFunctionReturn(PETSC_SUCCESS);
700: }
702: extern PetscErrorCode MatSeqAIJFactorInfo_Matlab(Mat, PetscViewer);
704: static PetscErrorCode MatView_SeqAIJ_ASCII(Mat A, PetscViewer viewer)
705: {
706: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
707: const PetscScalar *av;
708: PetscInt i, j, m = A->rmap->n;
709: const char *name;
710: PetscViewerFormat format;
712: PetscFunctionBegin;
713: if (A->structure_only) {
714: PetscCall(MatView_SeqAIJ_ASCII_structonly(A, viewer));
715: PetscFunctionReturn(PETSC_SUCCESS);
716: }
718: PetscCall(PetscViewerGetFormat(viewer, &format));
719: if (format == PETSC_VIEWER_ASCII_FACTOR_INFO || format == PETSC_VIEWER_ASCII_INFO || format == PETSC_VIEWER_ASCII_INFO_DETAIL) PetscFunctionReturn(PETSC_SUCCESS);
721: /* trigger copy to CPU if needed */
722: PetscCall(MatSeqAIJGetArrayRead(A, &av));
723: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
724: if (format == PETSC_VIEWER_ASCII_MATLAB) {
725: PetscInt nofinalvalue = 0;
726: if (m && ((a->i[m] == a->i[m - 1]) || (a->j[a->nz - 1] != A->cmap->n - 1))) {
727: /* Need a dummy value to ensure the dimension of the matrix. */
728: nofinalvalue = 1;
729: }
730: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
731: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Size = %" PetscInt_FMT " %" PetscInt_FMT " \n", m, A->cmap->n));
732: PetscCall(PetscViewerASCIIPrintf(viewer, "%% Nonzeros = %" PetscInt_FMT " \n", a->nz));
733: #if defined(PETSC_USE_COMPLEX)
734: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",4);\n", a->nz + nofinalvalue));
735: #else
736: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = zeros(%" PetscInt_FMT ",3);\n", a->nz + nofinalvalue));
737: #endif
738: PetscCall(PetscViewerASCIIPrintf(viewer, "zzz = [\n"));
740: for (i = 0; i < m; i++) {
741: for (j = a->i[i]; j < a->i[i + 1]; j++) {
742: #if defined(PETSC_USE_COMPLEX)
743: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", i + 1, a->j[j] + 1, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
744: #else
745: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", i + 1, a->j[j] + 1, (double)a->a[j]));
746: #endif
747: }
748: }
749: if (nofinalvalue) {
750: #if defined(PETSC_USE_COMPLEX)
751: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e %18.16e\n", m, A->cmap->n, 0., 0.));
752: #else
753: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %18.16e\n", m, A->cmap->n, 0.0));
754: #endif
755: }
756: PetscCall(PetscObjectGetName((PetscObject)A, &name));
757: PetscCall(PetscViewerASCIIPrintf(viewer, "];\n %s = spconvert(zzz);\n", name));
758: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
759: } else if (format == PETSC_VIEWER_ASCII_COMMON) {
760: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
761: for (i = 0; i < m; i++) {
762: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
763: for (j = a->i[i]; j < a->i[i + 1]; j++) {
764: #if defined(PETSC_USE_COMPLEX)
765: if (PetscImaginaryPart(a->a[j]) > 0.0 && PetscRealPart(a->a[j]) != 0.0) {
766: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
767: } else if (PetscImaginaryPart(a->a[j]) < 0.0 && PetscRealPart(a->a[j]) != 0.0) {
768: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
769: } else if (PetscRealPart(a->a[j]) != 0.0) {
770: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
771: }
772: #else
773: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
774: #endif
775: }
776: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
777: }
778: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
779: } else if (format == PETSC_VIEWER_ASCII_SYMMODU) {
780: PetscInt nzd = 0, fshift = 1, *sptr;
781: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
782: PetscCall(PetscMalloc1(m + 1, &sptr));
783: for (i = 0; i < m; i++) {
784: sptr[i] = nzd + 1;
785: for (j = a->i[i]; j < a->i[i + 1]; j++) {
786: if (a->j[j] >= i) {
787: #if defined(PETSC_USE_COMPLEX)
788: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) nzd++;
789: #else
790: if (a->a[j] != 0.0) nzd++;
791: #endif
792: }
793: }
794: }
795: sptr[m] = nzd + 1;
796: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n\n", m, nzd));
797: for (i = 0; i < m + 1; i += 6) {
798: if (i + 4 < m) {
799: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4], sptr[i + 5]));
800: } else if (i + 3 < m) {
801: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3], sptr[i + 4]));
802: } else if (i + 2 < m) {
803: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2], sptr[i + 3]));
804: } else if (i + 1 < m) {
805: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1], sptr[i + 2]));
806: } else if (i < m) {
807: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " %" PetscInt_FMT "\n", sptr[i], sptr[i + 1]));
808: } else {
809: PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT "\n", sptr[i]));
810: }
811: }
812: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
813: PetscCall(PetscFree(sptr));
814: for (i = 0; i < m; i++) {
815: for (j = a->i[i]; j < a->i[i + 1]; j++) {
816: if (a->j[j] >= i) PetscCall(PetscViewerASCIIPrintf(viewer, " %" PetscInt_FMT " ", a->j[j] + fshift));
817: }
818: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
819: }
820: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
821: for (i = 0; i < m; i++) {
822: for (j = a->i[i]; j < a->i[i + 1]; j++) {
823: if (a->j[j] >= i) {
824: #if defined(PETSC_USE_COMPLEX)
825: if (PetscImaginaryPart(a->a[j]) != 0.0 || PetscRealPart(a->a[j]) != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e %18.16e ", (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
826: #else
827: if (a->a[j] != 0.0) PetscCall(PetscViewerASCIIPrintf(viewer, " %18.16e ", (double)a->a[j]));
828: #endif
829: }
830: }
831: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
832: }
833: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
834: } else if (format == PETSC_VIEWER_ASCII_DENSE) {
835: PetscInt cnt = 0, jcnt;
836: PetscScalar value;
837: #if defined(PETSC_USE_COMPLEX)
838: PetscBool realonly = PETSC_TRUE;
840: for (i = 0; i < a->i[m]; i++) {
841: if (PetscImaginaryPart(a->a[i]) != 0.0) {
842: realonly = PETSC_FALSE;
843: break;
844: }
845: }
846: #endif
848: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
849: for (i = 0; i < m; i++) {
850: jcnt = 0;
851: for (j = 0; j < A->cmap->n; j++) {
852: if (jcnt < a->i[i + 1] - a->i[i] && j == a->j[cnt]) {
853: value = a->a[cnt++];
854: jcnt++;
855: } else {
856: value = 0.0;
857: }
858: #if defined(PETSC_USE_COMPLEX)
859: if (realonly) {
860: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)PetscRealPart(value)));
861: } else {
862: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e+%7.5e i ", (double)PetscRealPart(value), (double)PetscImaginaryPart(value)));
863: }
864: #else
865: PetscCall(PetscViewerASCIIPrintf(viewer, " %7.5e ", (double)value));
866: #endif
867: }
868: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
869: }
870: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
871: } else if (format == PETSC_VIEWER_ASCII_MATRIXMARKET) {
872: PetscInt fshift = 1;
873: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
874: #if defined(PETSC_USE_COMPLEX)
875: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate complex general\n"));
876: #else
877: PetscCall(PetscViewerASCIIPrintf(viewer, "%%%%MatrixMarket matrix coordinate real general\n"));
878: #endif
879: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %" PetscInt_FMT "\n", m, A->cmap->n, a->nz));
880: for (i = 0; i < m; i++) {
881: for (j = a->i[i]; j < a->i[i + 1]; j++) {
882: #if defined(PETSC_USE_COMPLEX)
883: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g %g\n", i + fshift, a->j[j] + fshift, (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
884: #else
885: PetscCall(PetscViewerASCIIPrintf(viewer, "%" PetscInt_FMT " %" PetscInt_FMT " %g\n", i + fshift, a->j[j] + fshift, (double)a->a[j]));
886: #endif
887: }
888: }
889: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
890: } else {
891: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_FALSE));
892: if (A->factortype) {
893: for (i = 0; i < m; i++) {
894: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
895: /* L part */
896: for (j = a->i[i]; j < a->i[i + 1]; j++) {
897: #if defined(PETSC_USE_COMPLEX)
898: if (PetscImaginaryPart(a->a[j]) > 0.0) {
899: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
900: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
901: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
902: } else {
903: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
904: }
905: #else
906: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
907: #endif
908: }
909: /* diagonal */
910: j = a->diag[i];
911: #if defined(PETSC_USE_COMPLEX)
912: if (PetscImaginaryPart(a->a[j]) > 0.0) {
913: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(1.0 / a->a[j]), (double)PetscImaginaryPart(1.0 / a->a[j])));
914: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
915: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(1.0 / a->a[j]), (double)(-PetscImaginaryPart(1.0 / a->a[j]))));
916: } else {
917: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(1.0 / a->a[j])));
918: }
919: #else
920: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)(1.0 / a->a[j])));
921: #endif
923: /* U part */
924: for (j = a->diag[i + 1] + 1; j < a->diag[i]; j++) {
925: #if defined(PETSC_USE_COMPLEX)
926: if (PetscImaginaryPart(a->a[j]) > 0.0) {
927: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
928: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
929: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)(-PetscImaginaryPart(a->a[j]))));
930: } else {
931: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
932: }
933: #else
934: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
935: #endif
936: }
937: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
938: }
939: } else {
940: for (i = 0; i < m; i++) {
941: PetscCall(PetscViewerASCIIPrintf(viewer, "row %" PetscInt_FMT ":", i));
942: for (j = a->i[i]; j < a->i[i + 1]; j++) {
943: #if defined(PETSC_USE_COMPLEX)
944: if (PetscImaginaryPart(a->a[j]) > 0.0) {
945: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g + %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)PetscImaginaryPart(a->a[j])));
946: } else if (PetscImaginaryPart(a->a[j]) < 0.0) {
947: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g - %g i)", a->j[j], (double)PetscRealPart(a->a[j]), (double)-PetscImaginaryPart(a->a[j])));
948: } else {
949: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)PetscRealPart(a->a[j])));
950: }
951: #else
952: PetscCall(PetscViewerASCIIPrintf(viewer, " (%" PetscInt_FMT ", %g) ", a->j[j], (double)a->a[j]));
953: #endif
954: }
955: PetscCall(PetscViewerASCIIPrintf(viewer, "\n"));
956: }
957: }
958: PetscCall(PetscViewerASCIIUseTabs(viewer, PETSC_TRUE));
959: }
960: PetscCall(PetscViewerFlush(viewer));
961: PetscFunctionReturn(PETSC_SUCCESS);
962: }
964: #include <petscdraw.h>
965: static PetscErrorCode MatView_SeqAIJ_Draw_Zoom(PetscDraw draw, void *Aa)
966: {
967: Mat A = (Mat)Aa;
968: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
969: PetscInt i, j, m = A->rmap->n;
970: int color;
971: PetscReal xl, yl, xr, yr, x_l, x_r, y_l, y_r;
972: PetscViewer viewer;
973: PetscViewerFormat format;
974: const PetscScalar *aa;
976: PetscFunctionBegin;
977: PetscCall(PetscObjectQuery((PetscObject)A, "Zoomviewer", (PetscObject *)&viewer));
978: PetscCall(PetscViewerGetFormat(viewer, &format));
979: PetscCall(PetscDrawGetCoordinates(draw, &xl, &yl, &xr, &yr));
981: /* loop over matrix elements drawing boxes */
982: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
983: if (format != PETSC_VIEWER_DRAW_CONTOUR) {
984: PetscDrawCollectiveBegin(draw);
985: /* Blue for negative, Cyan for zero and Red for positive */
986: color = PETSC_DRAW_BLUE;
987: for (i = 0; i < m; i++) {
988: y_l = m - i - 1.0;
989: y_r = y_l + 1.0;
990: for (j = a->i[i]; j < a->i[i + 1]; j++) {
991: x_l = a->j[j];
992: x_r = x_l + 1.0;
993: if (PetscRealPart(aa[j]) >= 0.) continue;
994: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
995: }
996: }
997: color = PETSC_DRAW_CYAN;
998: for (i = 0; i < m; i++) {
999: y_l = m - i - 1.0;
1000: y_r = y_l + 1.0;
1001: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1002: x_l = a->j[j];
1003: x_r = x_l + 1.0;
1004: if (aa[j] != 0.) continue;
1005: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1006: }
1007: }
1008: color = PETSC_DRAW_RED;
1009: for (i = 0; i < m; i++) {
1010: y_l = m - i - 1.0;
1011: y_r = y_l + 1.0;
1012: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1013: x_l = a->j[j];
1014: x_r = x_l + 1.0;
1015: if (PetscRealPart(aa[j]) <= 0.) continue;
1016: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1017: }
1018: }
1019: PetscDrawCollectiveEnd(draw);
1020: } else {
1021: /* use contour shading to indicate magnitude of values */
1022: /* first determine max of all nonzero values */
1023: PetscReal minv = 0.0, maxv = 0.0;
1024: PetscInt nz = a->nz, count = 0;
1025: PetscDraw popup;
1027: for (i = 0; i < nz; i++) {
1028: if (PetscAbsScalar(aa[i]) > maxv) maxv = PetscAbsScalar(aa[i]);
1029: }
1030: if (minv >= maxv) maxv = minv + PETSC_SMALL;
1031: PetscCall(PetscDrawGetPopup(draw, &popup));
1032: PetscCall(PetscDrawScalePopup(popup, minv, maxv));
1034: PetscDrawCollectiveBegin(draw);
1035: for (i = 0; i < m; i++) {
1036: y_l = m - i - 1.0;
1037: y_r = y_l + 1.0;
1038: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1039: x_l = a->j[j];
1040: x_r = x_l + 1.0;
1041: color = PetscDrawRealToColor(PetscAbsScalar(aa[count]), minv, maxv);
1042: PetscCall(PetscDrawRectangle(draw, x_l, y_l, x_r, y_r, color, color, color, color));
1043: count++;
1044: }
1045: }
1046: PetscDrawCollectiveEnd(draw);
1047: }
1048: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1049: PetscFunctionReturn(PETSC_SUCCESS);
1050: }
1052: #include <petscdraw.h>
1053: static PetscErrorCode MatView_SeqAIJ_Draw(Mat A, PetscViewer viewer)
1054: {
1055: PetscDraw draw;
1056: PetscReal xr, yr, xl, yl, h, w;
1057: PetscBool isnull;
1059: PetscFunctionBegin;
1060: PetscCall(PetscViewerDrawGetDraw(viewer, 0, &draw));
1061: PetscCall(PetscDrawIsNull(draw, &isnull));
1062: if (isnull) PetscFunctionReturn(PETSC_SUCCESS);
1064: xr = A->cmap->n;
1065: yr = A->rmap->n;
1066: h = yr / 10.0;
1067: w = xr / 10.0;
1068: xr += w;
1069: yr += h;
1070: xl = -w;
1071: yl = -h;
1072: PetscCall(PetscDrawSetCoordinates(draw, xl, yl, xr, yr));
1073: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", (PetscObject)viewer));
1074: PetscCall(PetscDrawZoom(draw, MatView_SeqAIJ_Draw_Zoom, A));
1075: PetscCall(PetscObjectCompose((PetscObject)A, "Zoomviewer", NULL));
1076: PetscCall(PetscDrawSave(draw));
1077: PetscFunctionReturn(PETSC_SUCCESS);
1078: }
1080: PetscErrorCode MatView_SeqAIJ(Mat A, PetscViewer viewer)
1081: {
1082: PetscBool iascii, isbinary, isdraw;
1084: PetscFunctionBegin;
1085: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERASCII, &iascii));
1086: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
1087: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERDRAW, &isdraw));
1088: if (iascii) PetscCall(MatView_SeqAIJ_ASCII(A, viewer));
1089: else if (isbinary) PetscCall(MatView_SeqAIJ_Binary(A, viewer));
1090: else if (isdraw) PetscCall(MatView_SeqAIJ_Draw(A, viewer));
1091: PetscCall(MatView_SeqAIJ_Inode(A, viewer));
1092: PetscFunctionReturn(PETSC_SUCCESS);
1093: }
1095: PetscErrorCode MatAssemblyEnd_SeqAIJ(Mat A, MatAssemblyType mode)
1096: {
1097: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1098: PetscInt fshift = 0, i, *ai = a->i, *aj = a->j, *imax = a->imax;
1099: PetscInt m = A->rmap->n, *ip, N, *ailen = a->ilen, rmax = 0, n;
1100: MatScalar *aa = a->a, *ap;
1101: PetscReal ratio = 0.6;
1103: PetscFunctionBegin;
1104: if (mode == MAT_FLUSH_ASSEMBLY) PetscFunctionReturn(PETSC_SUCCESS);
1105: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1106: if (A->was_assembled && A->ass_nonzerostate == A->nonzerostate) {
1107: /* we need to respect users asking to use or not the inodes routine in between matrix assemblies */
1108: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1109: PetscFunctionReturn(PETSC_SUCCESS);
1110: }
1112: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
1113: for (i = 1; i < m; i++) {
1114: /* move each row back by the amount of empty slots (fshift) before it*/
1115: fshift += imax[i - 1] - ailen[i - 1];
1116: rmax = PetscMax(rmax, ailen[i]);
1117: if (fshift) {
1118: ip = aj + ai[i];
1119: ap = aa + ai[i];
1120: N = ailen[i];
1121: PetscCall(PetscArraymove(ip - fshift, ip, N));
1122: if (!A->structure_only) PetscCall(PetscArraymove(ap - fshift, ap, N));
1123: }
1124: ai[i] = ai[i - 1] + ailen[i - 1];
1125: }
1126: if (m) {
1127: fshift += imax[m - 1] - ailen[m - 1];
1128: ai[m] = ai[m - 1] + ailen[m - 1];
1129: }
1130: /* reset ilen and imax for each row */
1131: a->nonzerorowcnt = 0;
1132: if (A->structure_only) {
1133: PetscCall(PetscFree(a->imax));
1134: PetscCall(PetscFree(a->ilen));
1135: } else { /* !A->structure_only */
1136: for (i = 0; i < m; i++) {
1137: ailen[i] = imax[i] = ai[i + 1] - ai[i];
1138: a->nonzerorowcnt += ((ai[i + 1] - ai[i]) > 0);
1139: }
1140: }
1141: a->nz = ai[m];
1142: PetscCheck(!fshift || a->nounused != -1, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Unused space detected in matrix: %" PetscInt_FMT " X %" PetscInt_FMT ", %" PetscInt_FMT " unneeded", m, A->cmap->n, fshift);
1143: PetscCall(MatMarkDiagonal_SeqAIJ(A)); // since diagonal info is used a lot, it is helpful to set them up at the end of assembly
1144: a->diagonaldense = PETSC_TRUE;
1145: n = PetscMin(A->rmap->n, A->cmap->n);
1146: for (i = 0; i < n; i++) {
1147: if (a->diag[i] >= ai[i + 1]) {
1148: a->diagonaldense = PETSC_FALSE;
1149: break;
1150: }
1151: }
1152: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; storage space: %" PetscInt_FMT " unneeded,%" PetscInt_FMT " used\n", m, A->cmap->n, fshift, a->nz));
1153: PetscCall(PetscInfo(A, "Number of mallocs during MatSetValues() is %" PetscInt_FMT "\n", a->reallocs));
1154: PetscCall(PetscInfo(A, "Maximum nonzeros in any row is %" PetscInt_FMT "\n", rmax));
1156: A->info.mallocs += a->reallocs;
1157: a->reallocs = 0;
1158: A->info.nz_unneeded = (PetscReal)fshift;
1159: a->rmax = rmax;
1161: if (!A->structure_only) PetscCall(MatCheckCompressedRow(A, a->nonzerorowcnt, &a->compressedrow, a->i, m, ratio));
1162: PetscCall(MatAssemblyEnd_SeqAIJ_Inode(A, mode));
1163: PetscFunctionReturn(PETSC_SUCCESS);
1164: }
1166: static PetscErrorCode MatRealPart_SeqAIJ(Mat A)
1167: {
1168: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1169: PetscInt i, nz = a->nz;
1170: MatScalar *aa;
1172: PetscFunctionBegin;
1173: PetscCall(MatSeqAIJGetArray(A, &aa));
1174: for (i = 0; i < nz; i++) aa[i] = PetscRealPart(aa[i]);
1175: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1176: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1177: PetscFunctionReturn(PETSC_SUCCESS);
1178: }
1180: static PetscErrorCode MatImaginaryPart_SeqAIJ(Mat A)
1181: {
1182: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1183: PetscInt i, nz = a->nz;
1184: MatScalar *aa;
1186: PetscFunctionBegin;
1187: PetscCall(MatSeqAIJGetArray(A, &aa));
1188: for (i = 0; i < nz; i++) aa[i] = PetscImaginaryPart(aa[i]);
1189: PetscCall(MatSeqAIJRestoreArray(A, &aa));
1190: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1191: PetscFunctionReturn(PETSC_SUCCESS);
1192: }
1194: PetscErrorCode MatZeroEntries_SeqAIJ(Mat A)
1195: {
1196: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1197: MatScalar *aa;
1199: PetscFunctionBegin;
1200: PetscCall(MatSeqAIJGetArrayWrite(A, &aa));
1201: PetscCall(PetscArrayzero(aa, a->i[A->rmap->n]));
1202: PetscCall(MatSeqAIJRestoreArrayWrite(A, &aa));
1203: PetscCall(MatSeqAIJInvalidateDiagonal(A));
1204: PetscFunctionReturn(PETSC_SUCCESS);
1205: }
1207: PetscErrorCode MatDestroy_SeqAIJ(Mat A)
1208: {
1209: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1211: PetscFunctionBegin;
1212: if (A->hash_active) {
1213: A->ops[0] = a->cops;
1214: PetscCall(PetscHMapIJVDestroy(&a->ht));
1215: PetscCall(PetscFree(a->dnz));
1216: A->hash_active = PETSC_FALSE;
1217: }
1219: PetscCall(PetscLogObjectState((PetscObject)A, "Rows=%" PetscInt_FMT ", Cols=%" PetscInt_FMT ", NZ=%" PetscInt_FMT, A->rmap->n, A->cmap->n, a->nz));
1220: PetscCall(MatSeqXAIJFreeAIJ(A, &a->a, &a->j, &a->i));
1221: PetscCall(ISDestroy(&a->row));
1222: PetscCall(ISDestroy(&a->col));
1223: PetscCall(PetscFree(a->diag));
1224: PetscCall(PetscFree(a->ibdiag));
1225: PetscCall(PetscFree(a->imax));
1226: PetscCall(PetscFree(a->ilen));
1227: PetscCall(PetscFree(a->ipre));
1228: PetscCall(PetscFree3(a->idiag, a->mdiag, a->ssor_work));
1229: PetscCall(PetscFree(a->solve_work));
1230: PetscCall(ISDestroy(&a->icol));
1231: PetscCall(PetscFree(a->saved_values));
1232: PetscCall(PetscFree2(a->compressedrow.i, a->compressedrow.rindex));
1233: PetscCall(MatDestroy_SeqAIJ_Inode(A));
1234: PetscCall(PetscFree(A->data));
1236: /* MatMatMultNumeric_SeqAIJ_SeqAIJ_Sorted may allocate this.
1237: That function is so heavily used (sometimes in an hidden way through multnumeric function pointers)
1238: that is hard to properly add this data to the MatProduct data. We free it here to avoid
1239: users reusing the matrix object with different data to incur in obscure segmentation faults
1240: due to different matrix sizes */
1241: PetscCall(PetscObjectCompose((PetscObject)A, "__PETSc__ab_dense", NULL));
1243: PetscCall(PetscObjectChangeTypeName((PetscObject)A, NULL));
1244: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEnginePut_C", NULL));
1245: PetscCall(PetscObjectComposeFunction((PetscObject)A, "PetscMatlabEngineGet_C", NULL));
1246: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetColumnIndices_C", NULL));
1247: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatStoreValues_C", NULL));
1248: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatRetrieveValues_C", NULL));
1249: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsbaij_C", NULL));
1250: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqbaij_C", NULL));
1251: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijperm_C", NULL));
1252: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijsell_C", NULL));
1253: #if defined(PETSC_HAVE_MKL_SPARSE)
1254: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijmkl_C", NULL));
1255: #endif
1256: #if defined(PETSC_HAVE_CUDA)
1257: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcusparse_C", NULL));
1258: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", NULL));
1259: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", NULL));
1260: #endif
1261: #if defined(PETSC_HAVE_HIP)
1262: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijhipsparse_C", NULL));
1263: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", NULL));
1264: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", NULL));
1265: #endif
1266: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
1267: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijkokkos_C", NULL));
1268: #endif
1269: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijcrl_C", NULL));
1270: #if defined(PETSC_HAVE_ELEMENTAL)
1271: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_elemental_C", NULL));
1272: #endif
1273: #if defined(PETSC_HAVE_SCALAPACK)
1274: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_scalapack_C", NULL));
1275: #endif
1276: #if defined(PETSC_HAVE_HYPRE)
1277: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_hypre_C", NULL));
1278: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", NULL));
1279: #endif
1280: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqdense_C", NULL));
1281: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqsell_C", NULL));
1282: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_is_C", NULL));
1283: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsTranspose_C", NULL));
1284: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatIsHermitianTranspose_C", NULL));
1285: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocation_C", NULL));
1286: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatResetPreallocation_C", NULL));
1287: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJSetPreallocationCSR_C", NULL));
1288: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatReorderForNonzeroDiagonal_C", NULL));
1289: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_is_seqaij_C", NULL));
1290: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqdense_seqaij_C", NULL));
1291: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaij_seqaij_C", NULL));
1292: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSeqAIJKron_C", NULL));
1293: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetPreallocationCOO_C", NULL));
1294: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatSetValuesCOO_C", NULL));
1295: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatFactorGetSolverType_C", NULL));
1296: /* these calls do not belong here: the subclasses Duplicate/Destroy are wrong */
1297: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijsell_seqaij_C", NULL));
1298: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaijperm_seqaij_C", NULL));
1299: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatConvert_seqaij_seqaijviennacl_C", NULL));
1300: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqdense_C", NULL));
1301: PetscCall(PetscObjectComposeFunction((PetscObject)A, "MatProductSetFromOptions_seqaijviennacl_seqaij_C", NULL));
1302: PetscFunctionReturn(PETSC_SUCCESS);
1303: }
1305: PetscErrorCode MatSetOption_SeqAIJ(Mat A, MatOption op, PetscBool flg)
1306: {
1307: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1309: PetscFunctionBegin;
1310: switch (op) {
1311: case MAT_ROW_ORIENTED:
1312: a->roworiented = flg;
1313: break;
1314: case MAT_KEEP_NONZERO_PATTERN:
1315: a->keepnonzeropattern = flg;
1316: break;
1317: case MAT_NEW_NONZERO_LOCATIONS:
1318: a->nonew = (flg ? 0 : 1);
1319: break;
1320: case MAT_NEW_NONZERO_LOCATION_ERR:
1321: a->nonew = (flg ? -1 : 0);
1322: break;
1323: case MAT_NEW_NONZERO_ALLOCATION_ERR:
1324: a->nonew = (flg ? -2 : 0);
1325: break;
1326: case MAT_UNUSED_NONZERO_LOCATION_ERR:
1327: a->nounused = (flg ? -1 : 0);
1328: break;
1329: case MAT_IGNORE_ZERO_ENTRIES:
1330: a->ignorezeroentries = flg;
1331: break;
1332: case MAT_SPD:
1333: case MAT_SYMMETRIC:
1334: case MAT_STRUCTURALLY_SYMMETRIC:
1335: case MAT_HERMITIAN:
1336: case MAT_SYMMETRY_ETERNAL:
1337: case MAT_STRUCTURE_ONLY:
1338: case MAT_STRUCTURAL_SYMMETRY_ETERNAL:
1339: case MAT_SPD_ETERNAL:
1340: /* if the diagonal matrix is square it inherits some of the properties above */
1341: break;
1342: case MAT_FORCE_DIAGONAL_ENTRIES:
1343: case MAT_IGNORE_OFF_PROC_ENTRIES:
1344: case MAT_USE_HASH_TABLE:
1345: PetscCall(PetscInfo(A, "Option %s ignored\n", MatOptions[op]));
1346: break;
1347: case MAT_USE_INODES:
1348: PetscCall(MatSetOption_SeqAIJ_Inode(A, MAT_USE_INODES, flg));
1349: break;
1350: case MAT_SUBMAT_SINGLEIS:
1351: A->submat_singleis = flg;
1352: break;
1353: case MAT_SORTED_FULL:
1354: if (flg) A->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
1355: else A->ops->setvalues = MatSetValues_SeqAIJ;
1356: break;
1357: case MAT_FORM_EXPLICIT_TRANSPOSE:
1358: A->form_explicit_transpose = flg;
1359: break;
1360: default:
1361: SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "unknown option %d", op);
1362: }
1363: PetscFunctionReturn(PETSC_SUCCESS);
1364: }
1366: static PetscErrorCode MatGetDiagonal_SeqAIJ(Mat A, Vec v)
1367: {
1368: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1369: PetscInt i, j, n, *ai = a->i, *aj = a->j;
1370: PetscScalar *x;
1371: const PetscScalar *aa;
1373: PetscFunctionBegin;
1374: PetscCall(VecGetLocalSize(v, &n));
1375: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
1376: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1377: if (A->factortype == MAT_FACTOR_ILU || A->factortype == MAT_FACTOR_LU) {
1378: PetscInt *diag = a->diag;
1379: PetscCall(VecGetArrayWrite(v, &x));
1380: for (i = 0; i < n; i++) x[i] = 1.0 / aa[diag[i]];
1381: PetscCall(VecRestoreArrayWrite(v, &x));
1382: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1383: PetscFunctionReturn(PETSC_SUCCESS);
1384: }
1386: PetscCall(VecGetArrayWrite(v, &x));
1387: for (i = 0; i < n; i++) {
1388: x[i] = 0.0;
1389: for (j = ai[i]; j < ai[i + 1]; j++) {
1390: if (aj[j] == i) {
1391: x[i] = aa[j];
1392: break;
1393: }
1394: }
1395: }
1396: PetscCall(VecRestoreArrayWrite(v, &x));
1397: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1398: PetscFunctionReturn(PETSC_SUCCESS);
1399: }
1401: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1402: PetscErrorCode MatMultTransposeAdd_SeqAIJ(Mat A, Vec xx, Vec zz, Vec yy)
1403: {
1404: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1405: const MatScalar *aa;
1406: PetscScalar *y;
1407: const PetscScalar *x;
1408: PetscInt m = A->rmap->n;
1409: #if !defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1410: const MatScalar *v;
1411: PetscScalar alpha;
1412: PetscInt n, i, j;
1413: const PetscInt *idx, *ii, *ridx = NULL;
1414: Mat_CompressedRow cprow = a->compressedrow;
1415: PetscBool usecprow = cprow.use;
1416: #endif
1418: PetscFunctionBegin;
1419: if (zz != yy) PetscCall(VecCopy(zz, yy));
1420: PetscCall(VecGetArrayRead(xx, &x));
1421: PetscCall(VecGetArray(yy, &y));
1422: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1424: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTTRANSPOSEAIJ)
1425: fortranmulttransposeaddaij_(&m, x, a->i, a->j, aa, y);
1426: #else
1427: if (usecprow) {
1428: m = cprow.nrows;
1429: ii = cprow.i;
1430: ridx = cprow.rindex;
1431: } else {
1432: ii = a->i;
1433: }
1434: for (i = 0; i < m; i++) {
1435: idx = a->j + ii[i];
1436: v = aa + ii[i];
1437: n = ii[i + 1] - ii[i];
1438: if (usecprow) {
1439: alpha = x[ridx[i]];
1440: } else {
1441: alpha = x[i];
1442: }
1443: for (j = 0; j < n; j++) y[idx[j]] += alpha * v[j];
1444: }
1445: #endif
1446: PetscCall(PetscLogFlops(2.0 * a->nz));
1447: PetscCall(VecRestoreArrayRead(xx, &x));
1448: PetscCall(VecRestoreArray(yy, &y));
1449: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1450: PetscFunctionReturn(PETSC_SUCCESS);
1451: }
1453: PetscErrorCode MatMultTranspose_SeqAIJ(Mat A, Vec xx, Vec yy)
1454: {
1455: PetscFunctionBegin;
1456: PetscCall(VecSet(yy, 0.0));
1457: PetscCall(MatMultTransposeAdd_SeqAIJ(A, xx, yy, yy));
1458: PetscFunctionReturn(PETSC_SUCCESS);
1459: }
1461: #include <../src/mat/impls/aij/seq/ftn-kernels/fmult.h>
1463: PetscErrorCode MatMult_SeqAIJ(Mat A, Vec xx, Vec yy)
1464: {
1465: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1466: PetscScalar *y;
1467: const PetscScalar *x;
1468: const MatScalar *aa, *a_a;
1469: PetscInt m = A->rmap->n;
1470: const PetscInt *aj, *ii, *ridx = NULL;
1471: PetscInt n, i;
1472: PetscScalar sum;
1473: PetscBool usecprow = a->compressedrow.use;
1475: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1476: #pragma disjoint(*x, *y, *aa)
1477: #endif
1479: PetscFunctionBegin;
1480: if (a->inode.use && a->inode.checked) {
1481: PetscCall(MatMult_SeqAIJ_Inode(A, xx, yy));
1482: PetscFunctionReturn(PETSC_SUCCESS);
1483: }
1484: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1485: PetscCall(VecGetArrayRead(xx, &x));
1486: PetscCall(VecGetArray(yy, &y));
1487: ii = a->i;
1488: if (usecprow) { /* use compressed row format */
1489: PetscCall(PetscArrayzero(y, m));
1490: m = a->compressedrow.nrows;
1491: ii = a->compressedrow.i;
1492: ridx = a->compressedrow.rindex;
1493: for (i = 0; i < m; i++) {
1494: n = ii[i + 1] - ii[i];
1495: aj = a->j + ii[i];
1496: aa = a_a + ii[i];
1497: sum = 0.0;
1498: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1499: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1500: y[*ridx++] = sum;
1501: }
1502: } else { /* do not use compressed row format */
1503: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTAIJ)
1504: aj = a->j;
1505: aa = a_a;
1506: fortranmultaij_(&m, x, ii, aj, aa, y);
1507: #else
1508: for (i = 0; i < m; i++) {
1509: n = ii[i + 1] - ii[i];
1510: aj = a->j + ii[i];
1511: aa = a_a + ii[i];
1512: sum = 0.0;
1513: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1514: y[i] = sum;
1515: }
1516: #endif
1517: }
1518: PetscCall(PetscLogFlops(2.0 * a->nz - a->nonzerorowcnt));
1519: PetscCall(VecRestoreArrayRead(xx, &x));
1520: PetscCall(VecRestoreArray(yy, &y));
1521: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1522: PetscFunctionReturn(PETSC_SUCCESS);
1523: }
1525: // HACK!!!!! Used by src/mat/tests/ex170.c
1526: PETSC_EXTERN PetscErrorCode MatMultMax_SeqAIJ(Mat A, Vec xx, Vec yy)
1527: {
1528: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1529: PetscScalar *y;
1530: const PetscScalar *x;
1531: const MatScalar *aa, *a_a;
1532: PetscInt m = A->rmap->n;
1533: const PetscInt *aj, *ii, *ridx = NULL;
1534: PetscInt n, i, nonzerorow = 0;
1535: PetscScalar sum;
1536: PetscBool usecprow = a->compressedrow.use;
1538: #if defined(PETSC_HAVE_PRAGMA_DISJOINT)
1539: #pragma disjoint(*x, *y, *aa)
1540: #endif
1542: PetscFunctionBegin;
1543: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1544: PetscCall(VecGetArrayRead(xx, &x));
1545: PetscCall(VecGetArray(yy, &y));
1546: if (usecprow) { /* use compressed row format */
1547: m = a->compressedrow.nrows;
1548: ii = a->compressedrow.i;
1549: ridx = a->compressedrow.rindex;
1550: for (i = 0; i < m; i++) {
1551: n = ii[i + 1] - ii[i];
1552: aj = a->j + ii[i];
1553: aa = a_a + ii[i];
1554: sum = 0.0;
1555: nonzerorow += (n > 0);
1556: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1557: /* for (j=0; j<n; j++) sum += (*aa++)*x[*aj++]; */
1558: y[*ridx++] = sum;
1559: }
1560: } else { /* do not use compressed row format */
1561: ii = a->i;
1562: for (i = 0; i < m; i++) {
1563: n = ii[i + 1] - ii[i];
1564: aj = a->j + ii[i];
1565: aa = a_a + ii[i];
1566: sum = 0.0;
1567: nonzerorow += (n > 0);
1568: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1569: y[i] = sum;
1570: }
1571: }
1572: PetscCall(PetscLogFlops(2.0 * a->nz - nonzerorow));
1573: PetscCall(VecRestoreArrayRead(xx, &x));
1574: PetscCall(VecRestoreArray(yy, &y));
1575: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1576: PetscFunctionReturn(PETSC_SUCCESS);
1577: }
1579: // HACK!!!!! Used by src/mat/tests/ex170.c
1580: PETSC_EXTERN PetscErrorCode MatMultAddMax_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1581: {
1582: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1583: PetscScalar *y, *z;
1584: const PetscScalar *x;
1585: const MatScalar *aa, *a_a;
1586: PetscInt m = A->rmap->n, *aj, *ii;
1587: PetscInt n, i, *ridx = NULL;
1588: PetscScalar sum;
1589: PetscBool usecprow = a->compressedrow.use;
1591: PetscFunctionBegin;
1592: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1593: PetscCall(VecGetArrayRead(xx, &x));
1594: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1595: if (usecprow) { /* use compressed row format */
1596: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1597: m = a->compressedrow.nrows;
1598: ii = a->compressedrow.i;
1599: ridx = a->compressedrow.rindex;
1600: for (i = 0; i < m; i++) {
1601: n = ii[i + 1] - ii[i];
1602: aj = a->j + ii[i];
1603: aa = a_a + ii[i];
1604: sum = y[*ridx];
1605: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1606: z[*ridx++] = sum;
1607: }
1608: } else { /* do not use compressed row format */
1609: ii = a->i;
1610: for (i = 0; i < m; i++) {
1611: n = ii[i + 1] - ii[i];
1612: aj = a->j + ii[i];
1613: aa = a_a + ii[i];
1614: sum = y[i];
1615: PetscSparseDenseMaxDot(sum, x, aa, aj, n);
1616: z[i] = sum;
1617: }
1618: }
1619: PetscCall(PetscLogFlops(2.0 * a->nz));
1620: PetscCall(VecRestoreArrayRead(xx, &x));
1621: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1622: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1623: PetscFunctionReturn(PETSC_SUCCESS);
1624: }
1626: #include <../src/mat/impls/aij/seq/ftn-kernels/fmultadd.h>
1627: PetscErrorCode MatMultAdd_SeqAIJ(Mat A, Vec xx, Vec yy, Vec zz)
1628: {
1629: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1630: PetscScalar *y, *z;
1631: const PetscScalar *x;
1632: const MatScalar *aa, *a_a;
1633: const PetscInt *aj, *ii, *ridx = NULL;
1634: PetscInt m = A->rmap->n, n, i;
1635: PetscScalar sum;
1636: PetscBool usecprow = a->compressedrow.use;
1638: PetscFunctionBegin;
1639: if (a->inode.use && a->inode.checked) {
1640: PetscCall(MatMultAdd_SeqAIJ_Inode(A, xx, yy, zz));
1641: PetscFunctionReturn(PETSC_SUCCESS);
1642: }
1643: PetscCall(MatSeqAIJGetArrayRead(A, &a_a));
1644: PetscCall(VecGetArrayRead(xx, &x));
1645: PetscCall(VecGetArrayPair(yy, zz, &y, &z));
1646: if (usecprow) { /* use compressed row format */
1647: if (zz != yy) PetscCall(PetscArraycpy(z, y, m));
1648: m = a->compressedrow.nrows;
1649: ii = a->compressedrow.i;
1650: ridx = a->compressedrow.rindex;
1651: for (i = 0; i < m; i++) {
1652: n = ii[i + 1] - ii[i];
1653: aj = a->j + ii[i];
1654: aa = a_a + ii[i];
1655: sum = y[*ridx];
1656: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1657: z[*ridx++] = sum;
1658: }
1659: } else { /* do not use compressed row format */
1660: ii = a->i;
1661: #if defined(PETSC_USE_FORTRAN_KERNEL_MULTADDAIJ)
1662: aj = a->j;
1663: aa = a_a;
1664: fortranmultaddaij_(&m, x, ii, aj, aa, y, z);
1665: #else
1666: for (i = 0; i < m; i++) {
1667: n = ii[i + 1] - ii[i];
1668: aj = a->j + ii[i];
1669: aa = a_a + ii[i];
1670: sum = y[i];
1671: PetscSparseDensePlusDot(sum, x, aa, aj, n);
1672: z[i] = sum;
1673: }
1674: #endif
1675: }
1676: PetscCall(PetscLogFlops(2.0 * a->nz));
1677: PetscCall(VecRestoreArrayRead(xx, &x));
1678: PetscCall(VecRestoreArrayPair(yy, zz, &y, &z));
1679: PetscCall(MatSeqAIJRestoreArrayRead(A, &a_a));
1680: PetscFunctionReturn(PETSC_SUCCESS);
1681: }
1683: /*
1684: Adds diagonal pointers to sparse matrix structure.
1685: */
1686: PetscErrorCode MatMarkDiagonal_SeqAIJ(Mat A)
1687: {
1688: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1689: PetscInt i, j, m = A->rmap->n;
1690: PetscBool alreadySet = PETSC_TRUE;
1692: PetscFunctionBegin;
1693: if (!a->diag) {
1694: PetscCall(PetscMalloc1(m, &a->diag));
1695: alreadySet = PETSC_FALSE;
1696: }
1697: for (i = 0; i < A->rmap->n; i++) {
1698: /* If A's diagonal is already correctly set, this fast track enables cheap and repeated MatMarkDiagonal_SeqAIJ() calls */
1699: if (alreadySet) {
1700: PetscInt pos = a->diag[i];
1701: if (pos >= a->i[i] && pos < a->i[i + 1] && a->j[pos] == i) continue;
1702: }
1704: a->diag[i] = a->i[i + 1];
1705: for (j = a->i[i]; j < a->i[i + 1]; j++) {
1706: if (a->j[j] == i) {
1707: a->diag[i] = j;
1708: break;
1709: }
1710: }
1711: }
1712: PetscFunctionReturn(PETSC_SUCCESS);
1713: }
1715: static PetscErrorCode MatShift_SeqAIJ(Mat A, PetscScalar v)
1716: {
1717: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1718: const PetscInt *diag = (const PetscInt *)a->diag;
1719: const PetscInt *ii = (const PetscInt *)a->i;
1720: PetscInt i, *mdiag = NULL;
1721: PetscInt cnt = 0; /* how many diagonals are missing */
1723: PetscFunctionBegin;
1724: if (!A->preallocated || !a->nz) {
1725: PetscCall(MatSeqAIJSetPreallocation(A, 1, NULL));
1726: PetscCall(MatShift_Basic(A, v));
1727: PetscFunctionReturn(PETSC_SUCCESS);
1728: }
1730: if (a->diagonaldense) {
1731: cnt = 0;
1732: } else {
1733: PetscCall(PetscCalloc1(A->rmap->n, &mdiag));
1734: for (i = 0; i < A->rmap->n; i++) {
1735: if (i < A->cmap->n && diag[i] >= ii[i + 1]) { /* 'out of range' rows never have diagonals */
1736: cnt++;
1737: mdiag[i] = 1;
1738: }
1739: }
1740: }
1741: if (!cnt) {
1742: PetscCall(MatShift_Basic(A, v));
1743: } else {
1744: PetscScalar *olda = a->a; /* preserve pointers to current matrix nonzeros structure and values */
1745: PetscInt *oldj = a->j, *oldi = a->i;
1746: PetscBool singlemalloc = a->singlemalloc, free_a = a->free_a, free_ij = a->free_ij;
1747: const PetscScalar *Aa;
1749: PetscCall(MatSeqAIJGetArrayRead(A, &Aa)); // sync the host
1750: PetscCall(MatSeqAIJRestoreArrayRead(A, &Aa));
1752: a->a = NULL;
1753: a->j = NULL;
1754: a->i = NULL;
1755: /* increase the values in imax for each row where a diagonal is being inserted then reallocate the matrix data structures */
1756: for (i = 0; i < PetscMin(A->rmap->n, A->cmap->n); i++) a->imax[i] += mdiag[i];
1757: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(A, 0, a->imax));
1759: /* copy old values into new matrix data structure */
1760: for (i = 0; i < A->rmap->n; i++) {
1761: PetscCall(MatSetValues(A, 1, &i, a->imax[i] - mdiag[i], &oldj[oldi[i]], &olda[oldi[i]], ADD_VALUES));
1762: if (i < A->cmap->n) PetscCall(MatSetValue(A, i, i, v, ADD_VALUES));
1763: }
1764: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
1765: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
1766: if (singlemalloc) {
1767: PetscCall(PetscFree3(olda, oldj, oldi));
1768: } else {
1769: if (free_a) PetscCall(PetscFree(olda));
1770: if (free_ij) PetscCall(PetscFree(oldj));
1771: if (free_ij) PetscCall(PetscFree(oldi));
1772: }
1773: }
1774: PetscCall(PetscFree(mdiag));
1775: a->diagonaldense = PETSC_TRUE;
1776: PetscFunctionReturn(PETSC_SUCCESS);
1777: }
1779: /*
1780: Checks for missing diagonals
1781: */
1782: PetscErrorCode MatMissingDiagonal_SeqAIJ(Mat A, PetscBool *missing, PetscInt *d)
1783: {
1784: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1785: PetscInt *diag, *ii = a->i, i;
1787: PetscFunctionBegin;
1788: *missing = PETSC_FALSE;
1789: if (A->rmap->n > 0 && !ii) {
1790: *missing = PETSC_TRUE;
1791: if (d) *d = 0;
1792: PetscCall(PetscInfo(A, "Matrix has no entries therefore is missing diagonal\n"));
1793: } else {
1794: PetscInt n;
1795: n = PetscMin(A->rmap->n, A->cmap->n);
1796: diag = a->diag;
1797: for (i = 0; i < n; i++) {
1798: if (diag[i] >= ii[i + 1]) {
1799: *missing = PETSC_TRUE;
1800: if (d) *d = i;
1801: PetscCall(PetscInfo(A, "Matrix is missing diagonal number %" PetscInt_FMT "\n", i));
1802: break;
1803: }
1804: }
1805: }
1806: PetscFunctionReturn(PETSC_SUCCESS);
1807: }
1809: #include <petscblaslapack.h>
1810: #include <petsc/private/kernels/blockinvert.h>
1812: /*
1813: Note that values is allocated externally by the PC and then passed into this routine
1814: */
1815: static PetscErrorCode MatInvertVariableBlockDiagonal_SeqAIJ(Mat A, PetscInt nblocks, const PetscInt *bsizes, PetscScalar *diag)
1816: {
1817: PetscInt n = A->rmap->n, i, ncnt = 0, *indx, j, bsizemax = 0, *v_pivots;
1818: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
1819: const PetscReal shift = 0.0;
1820: PetscInt ipvt[5];
1821: PetscCount flops = 0;
1822: PetscScalar work[25], *v_work;
1824: PetscFunctionBegin;
1825: allowzeropivot = PetscNot(A->erroriffailure);
1826: for (i = 0; i < nblocks; i++) ncnt += bsizes[i];
1827: PetscCheck(ncnt == n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Total blocksizes %" PetscInt_FMT " doesn't match number matrix rows %" PetscInt_FMT, ncnt, n);
1828: for (i = 0; i < nblocks; i++) bsizemax = PetscMax(bsizemax, bsizes[i]);
1829: PetscCall(PetscMalloc1(bsizemax, &indx));
1830: if (bsizemax > 7) PetscCall(PetscMalloc2(bsizemax, &v_work, bsizemax, &v_pivots));
1831: ncnt = 0;
1832: for (i = 0; i < nblocks; i++) {
1833: for (j = 0; j < bsizes[i]; j++) indx[j] = ncnt + j;
1834: PetscCall(MatGetValues(A, bsizes[i], indx, bsizes[i], indx, diag));
1835: switch (bsizes[i]) {
1836: case 1:
1837: *diag = 1.0 / (*diag);
1838: break;
1839: case 2:
1840: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
1841: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1842: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
1843: break;
1844: case 3:
1845: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
1846: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1847: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
1848: break;
1849: case 4:
1850: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
1851: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1852: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
1853: break;
1854: case 5:
1855: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
1856: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1857: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
1858: break;
1859: case 6:
1860: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
1861: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1862: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
1863: break;
1864: case 7:
1865: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
1866: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1867: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
1868: break;
1869: default:
1870: PetscCall(PetscKernel_A_gets_inverse_A(bsizes[i], diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
1871: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1872: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bsizes[i]));
1873: }
1874: ncnt += bsizes[i];
1875: diag += bsizes[i] * bsizes[i];
1876: flops += 2 * PetscPowInt(bsizes[i], 3) / 3;
1877: }
1878: PetscCall(PetscLogFlops(flops));
1879: if (bsizemax > 7) PetscCall(PetscFree2(v_work, v_pivots));
1880: PetscCall(PetscFree(indx));
1881: PetscFunctionReturn(PETSC_SUCCESS);
1882: }
1884: /*
1885: Negative shift indicates do not generate an error if there is a zero diagonal, just invert it anyways
1886: */
1887: static PetscErrorCode MatInvertDiagonal_SeqAIJ(Mat A, PetscScalar omega, PetscScalar fshift)
1888: {
1889: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1890: PetscInt i, *diag, m = A->rmap->n;
1891: const MatScalar *v;
1892: PetscScalar *idiag, *mdiag;
1894: PetscFunctionBegin;
1895: if (a->idiagvalid) PetscFunctionReturn(PETSC_SUCCESS);
1896: PetscCall(MatMarkDiagonal_SeqAIJ(A));
1897: diag = a->diag;
1898: if (!a->idiag) { PetscCall(PetscMalloc3(m, &a->idiag, m, &a->mdiag, m, &a->ssor_work)); }
1900: mdiag = a->mdiag;
1901: idiag = a->idiag;
1902: PetscCall(MatSeqAIJGetArrayRead(A, &v));
1903: if (omega == 1.0 && PetscRealPart(fshift) <= 0.0) {
1904: for (i = 0; i < m; i++) {
1905: mdiag[i] = v[diag[i]];
1906: if (!PetscAbsScalar(mdiag[i])) { /* zero diagonal */
1907: if (PetscRealPart(fshift)) {
1908: PetscCall(PetscInfo(A, "Zero diagonal on row %" PetscInt_FMT "\n", i));
1909: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
1910: A->factorerror_zeropivot_value = 0.0;
1911: A->factorerror_zeropivot_row = i;
1912: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Zero diagonal on row %" PetscInt_FMT, i);
1913: }
1914: idiag[i] = 1.0 / v[diag[i]];
1915: }
1916: PetscCall(PetscLogFlops(m));
1917: } else {
1918: for (i = 0; i < m; i++) {
1919: mdiag[i] = v[diag[i]];
1920: idiag[i] = omega / (fshift + v[diag[i]]);
1921: }
1922: PetscCall(PetscLogFlops(2.0 * m));
1923: }
1924: a->idiagvalid = PETSC_TRUE;
1925: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
1926: PetscFunctionReturn(PETSC_SUCCESS);
1927: }
1929: PetscErrorCode MatSOR_SeqAIJ(Mat A, Vec bb, PetscReal omega, MatSORType flag, PetscReal fshift, PetscInt its, PetscInt lits, Vec xx)
1930: {
1931: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
1932: PetscScalar *x, d, sum, *t, scale;
1933: const MatScalar *v, *idiag = NULL, *mdiag, *aa;
1934: const PetscScalar *b, *bs, *xb, *ts;
1935: PetscInt n, m = A->rmap->n, i;
1936: const PetscInt *idx, *diag;
1938: PetscFunctionBegin;
1939: if (a->inode.use && a->inode.checked && omega == 1.0 && fshift == 0.0) {
1940: PetscCall(MatSOR_SeqAIJ_Inode(A, bb, omega, flag, fshift, its, lits, xx));
1941: PetscFunctionReturn(PETSC_SUCCESS);
1942: }
1943: its = its * lits;
1945: if (fshift != a->fshift || omega != a->omega) a->idiagvalid = PETSC_FALSE; /* must recompute idiag[] */
1946: if (!a->idiagvalid) PetscCall(MatInvertDiagonal_SeqAIJ(A, omega, fshift));
1947: a->fshift = fshift;
1948: a->omega = omega;
1950: diag = a->diag;
1951: t = a->ssor_work;
1952: idiag = a->idiag;
1953: mdiag = a->mdiag;
1955: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
1956: PetscCall(VecGetArray(xx, &x));
1957: PetscCall(VecGetArrayRead(bb, &b));
1958: /* We count flops by assuming the upper triangular and lower triangular parts have the same number of nonzeros */
1959: if (flag == SOR_APPLY_UPPER) {
1960: /* apply (U + D/omega) to the vector */
1961: bs = b;
1962: for (i = 0; i < m; i++) {
1963: d = fshift + mdiag[i];
1964: n = a->i[i + 1] - diag[i] - 1;
1965: idx = a->j + diag[i] + 1;
1966: v = aa + diag[i] + 1;
1967: sum = b[i] * d / omega;
1968: PetscSparseDensePlusDot(sum, bs, v, idx, n);
1969: x[i] = sum;
1970: }
1971: PetscCall(VecRestoreArray(xx, &x));
1972: PetscCall(VecRestoreArrayRead(bb, &b));
1973: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
1974: PetscCall(PetscLogFlops(a->nz));
1975: PetscFunctionReturn(PETSC_SUCCESS);
1976: }
1978: PetscCheck(flag != SOR_APPLY_LOWER, PETSC_COMM_SELF, PETSC_ERR_SUP, "SOR_APPLY_LOWER is not implemented");
1979: if (flag & SOR_EISENSTAT) {
1980: /* Let A = L + U + D; where L is lower triangular,
1981: U is upper triangular, E = D/omega; This routine applies
1983: (L + E)^{-1} A (U + E)^{-1}
1985: to a vector efficiently using Eisenstat's trick.
1986: */
1987: scale = (2.0 / omega) - 1.0;
1989: /* x = (E + U)^{-1} b */
1990: for (i = m - 1; i >= 0; i--) {
1991: n = a->i[i + 1] - diag[i] - 1;
1992: idx = a->j + diag[i] + 1;
1993: v = aa + diag[i] + 1;
1994: sum = b[i];
1995: PetscSparseDenseMinusDot(sum, x, v, idx, n);
1996: x[i] = sum * idiag[i];
1997: }
1999: /* t = b - (2*E - D)x */
2000: v = aa;
2001: for (i = 0; i < m; i++) t[i] = b[i] - scale * (v[*diag++]) * x[i];
2003: /* t = (E + L)^{-1}t */
2004: ts = t;
2005: diag = a->diag;
2006: for (i = 0; i < m; i++) {
2007: n = diag[i] - a->i[i];
2008: idx = a->j + a->i[i];
2009: v = aa + a->i[i];
2010: sum = t[i];
2011: PetscSparseDenseMinusDot(sum, ts, v, idx, n);
2012: t[i] = sum * idiag[i];
2013: /* x = x + t */
2014: x[i] += t[i];
2015: }
2017: PetscCall(PetscLogFlops(6.0 * m - 1 + 2.0 * a->nz));
2018: PetscCall(VecRestoreArray(xx, &x));
2019: PetscCall(VecRestoreArrayRead(bb, &b));
2020: PetscFunctionReturn(PETSC_SUCCESS);
2021: }
2022: if (flag & SOR_ZERO_INITIAL_GUESS) {
2023: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2024: for (i = 0; i < m; i++) {
2025: n = diag[i] - a->i[i];
2026: idx = a->j + a->i[i];
2027: v = aa + a->i[i];
2028: sum = b[i];
2029: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2030: t[i] = sum;
2031: x[i] = sum * idiag[i];
2032: }
2033: xb = t;
2034: PetscCall(PetscLogFlops(a->nz));
2035: } else xb = b;
2036: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2037: for (i = m - 1; i >= 0; i--) {
2038: n = a->i[i + 1] - diag[i] - 1;
2039: idx = a->j + diag[i] + 1;
2040: v = aa + diag[i] + 1;
2041: sum = xb[i];
2042: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2043: if (xb == b) {
2044: x[i] = sum * idiag[i];
2045: } else {
2046: x[i] = (1 - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2047: }
2048: }
2049: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2050: }
2051: its--;
2052: }
2053: while (its--) {
2054: if (flag & SOR_FORWARD_SWEEP || flag & SOR_LOCAL_FORWARD_SWEEP) {
2055: for (i = 0; i < m; i++) {
2056: /* lower */
2057: n = diag[i] - a->i[i];
2058: idx = a->j + a->i[i];
2059: v = aa + a->i[i];
2060: sum = b[i];
2061: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2062: t[i] = sum; /* save application of the lower-triangular part */
2063: /* upper */
2064: n = a->i[i + 1] - diag[i] - 1;
2065: idx = a->j + diag[i] + 1;
2066: v = aa + diag[i] + 1;
2067: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2068: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2069: }
2070: xb = t;
2071: PetscCall(PetscLogFlops(2.0 * a->nz));
2072: } else xb = b;
2073: if (flag & SOR_BACKWARD_SWEEP || flag & SOR_LOCAL_BACKWARD_SWEEP) {
2074: for (i = m - 1; i >= 0; i--) {
2075: sum = xb[i];
2076: if (xb == b) {
2077: /* whole matrix (no checkpointing available) */
2078: n = a->i[i + 1] - a->i[i];
2079: idx = a->j + a->i[i];
2080: v = aa + a->i[i];
2081: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2082: x[i] = (1. - omega) * x[i] + (sum + mdiag[i] * x[i]) * idiag[i];
2083: } else { /* lower-triangular part has been saved, so only apply upper-triangular */
2084: n = a->i[i + 1] - diag[i] - 1;
2085: idx = a->j + diag[i] + 1;
2086: v = aa + diag[i] + 1;
2087: PetscSparseDenseMinusDot(sum, x, v, idx, n);
2088: x[i] = (1. - omega) * x[i] + sum * idiag[i]; /* omega in idiag */
2089: }
2090: }
2091: if (xb == b) {
2092: PetscCall(PetscLogFlops(2.0 * a->nz));
2093: } else {
2094: PetscCall(PetscLogFlops(a->nz)); /* assumes 1/2 in upper */
2095: }
2096: }
2097: }
2098: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2099: PetscCall(VecRestoreArray(xx, &x));
2100: PetscCall(VecRestoreArrayRead(bb, &b));
2101: PetscFunctionReturn(PETSC_SUCCESS);
2102: }
2104: static PetscErrorCode MatGetInfo_SeqAIJ(Mat A, MatInfoType flag, MatInfo *info)
2105: {
2106: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2108: PetscFunctionBegin;
2109: info->block_size = 1.0;
2110: info->nz_allocated = a->maxnz;
2111: info->nz_used = a->nz;
2112: info->nz_unneeded = (a->maxnz - a->nz);
2113: info->assemblies = A->num_ass;
2114: info->mallocs = A->info.mallocs;
2115: info->memory = 0; /* REVIEW ME */
2116: if (A->factortype) {
2117: info->fill_ratio_given = A->info.fill_ratio_given;
2118: info->fill_ratio_needed = A->info.fill_ratio_needed;
2119: info->factor_mallocs = A->info.factor_mallocs;
2120: } else {
2121: info->fill_ratio_given = 0;
2122: info->fill_ratio_needed = 0;
2123: info->factor_mallocs = 0;
2124: }
2125: PetscFunctionReturn(PETSC_SUCCESS);
2126: }
2128: static PetscErrorCode MatZeroRows_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2129: {
2130: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2131: PetscInt i, m = A->rmap->n - 1;
2132: const PetscScalar *xx;
2133: PetscScalar *bb, *aa;
2134: PetscInt d = 0;
2136: PetscFunctionBegin;
2137: if (x && b) {
2138: PetscCall(VecGetArrayRead(x, &xx));
2139: PetscCall(VecGetArray(b, &bb));
2140: for (i = 0; i < N; i++) {
2141: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2142: if (rows[i] >= A->cmap->n) continue;
2143: bb[rows[i]] = diag * xx[rows[i]];
2144: }
2145: PetscCall(VecRestoreArrayRead(x, &xx));
2146: PetscCall(VecRestoreArray(b, &bb));
2147: }
2149: PetscCall(MatSeqAIJGetArray(A, &aa));
2150: if (a->keepnonzeropattern) {
2151: for (i = 0; i < N; i++) {
2152: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2153: PetscCall(PetscArrayzero(&aa[a->i[rows[i]]], a->ilen[rows[i]]));
2154: }
2155: if (diag != 0.0) {
2156: for (i = 0; i < N; i++) {
2157: d = rows[i];
2158: if (rows[i] >= A->cmap->n) continue;
2159: PetscCheck(a->diag[d] < a->i[d + 1], PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in the zeroed row %" PetscInt_FMT, d);
2160: }
2161: for (i = 0; i < N; i++) {
2162: if (rows[i] >= A->cmap->n) continue;
2163: aa[a->diag[rows[i]]] = diag;
2164: }
2165: }
2166: } else {
2167: if (diag != 0.0) {
2168: for (i = 0; i < N; i++) {
2169: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2170: if (a->ilen[rows[i]] > 0) {
2171: if (rows[i] >= A->cmap->n) {
2172: a->ilen[rows[i]] = 0;
2173: } else {
2174: a->ilen[rows[i]] = 1;
2175: aa[a->i[rows[i]]] = diag;
2176: a->j[a->i[rows[i]]] = rows[i];
2177: }
2178: } else if (rows[i] < A->cmap->n) { /* in case row was completely empty */
2179: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2180: }
2181: }
2182: } else {
2183: for (i = 0; i < N; i++) {
2184: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2185: a->ilen[rows[i]] = 0;
2186: }
2187: }
2188: A->nonzerostate++;
2189: }
2190: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2191: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2192: PetscFunctionReturn(PETSC_SUCCESS);
2193: }
2195: static PetscErrorCode MatZeroRowsColumns_SeqAIJ(Mat A, PetscInt N, const PetscInt rows[], PetscScalar diag, Vec x, Vec b)
2196: {
2197: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2198: PetscInt i, j, m = A->rmap->n - 1, d = 0;
2199: PetscBool missing, *zeroed, vecs = PETSC_FALSE;
2200: const PetscScalar *xx;
2201: PetscScalar *bb, *aa;
2203: PetscFunctionBegin;
2204: if (!N) PetscFunctionReturn(PETSC_SUCCESS);
2205: PetscCall(MatSeqAIJGetArray(A, &aa));
2206: if (x && b) {
2207: PetscCall(VecGetArrayRead(x, &xx));
2208: PetscCall(VecGetArray(b, &bb));
2209: vecs = PETSC_TRUE;
2210: }
2211: PetscCall(PetscCalloc1(A->rmap->n, &zeroed));
2212: for (i = 0; i < N; i++) {
2213: PetscCheck(rows[i] >= 0 && rows[i] <= m, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "row %" PetscInt_FMT " out of range", rows[i]);
2214: PetscCall(PetscArrayzero(PetscSafePointerPlusOffset(aa, a->i[rows[i]]), a->ilen[rows[i]]));
2216: zeroed[rows[i]] = PETSC_TRUE;
2217: }
2218: for (i = 0; i < A->rmap->n; i++) {
2219: if (!zeroed[i]) {
2220: for (j = a->i[i]; j < a->i[i + 1]; j++) {
2221: if (a->j[j] < A->rmap->n && zeroed[a->j[j]]) {
2222: if (vecs) bb[i] -= aa[j] * xx[a->j[j]];
2223: aa[j] = 0.0;
2224: }
2225: }
2226: } else if (vecs && i < A->cmap->N) bb[i] = diag * xx[i];
2227: }
2228: if (x && b) {
2229: PetscCall(VecRestoreArrayRead(x, &xx));
2230: PetscCall(VecRestoreArray(b, &bb));
2231: }
2232: PetscCall(PetscFree(zeroed));
2233: if (diag != 0.0) {
2234: PetscCall(MatMissingDiagonal_SeqAIJ(A, &missing, &d));
2235: if (missing) {
2236: for (i = 0; i < N; i++) {
2237: if (rows[i] >= A->cmap->N) continue;
2238: PetscCheck(!a->nonew || rows[i] < d, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Matrix is missing diagonal entry in row %" PetscInt_FMT " (%" PetscInt_FMT ")", d, rows[i]);
2239: PetscCall(MatSetValues_SeqAIJ(A, 1, &rows[i], 1, &rows[i], &diag, INSERT_VALUES));
2240: }
2241: } else {
2242: for (i = 0; i < N; i++) aa[a->diag[rows[i]]] = diag;
2243: }
2244: }
2245: PetscCall(MatSeqAIJRestoreArray(A, &aa));
2246: PetscUseTypeMethod(A, assemblyend, MAT_FINAL_ASSEMBLY);
2247: PetscFunctionReturn(PETSC_SUCCESS);
2248: }
2250: PetscErrorCode MatGetRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2251: {
2252: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2253: const PetscScalar *aa;
2255: PetscFunctionBegin;
2256: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2257: *nz = a->i[row + 1] - a->i[row];
2258: if (v) *v = PetscSafePointerPlusOffset((PetscScalar *)aa, a->i[row]);
2259: if (idx) {
2260: if (*nz && a->j) *idx = a->j + a->i[row];
2261: else *idx = NULL;
2262: }
2263: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2264: PetscFunctionReturn(PETSC_SUCCESS);
2265: }
2267: PetscErrorCode MatRestoreRow_SeqAIJ(Mat A, PetscInt row, PetscInt *nz, PetscInt **idx, PetscScalar **v)
2268: {
2269: PetscFunctionBegin;
2270: PetscFunctionReturn(PETSC_SUCCESS);
2271: }
2273: static PetscErrorCode MatNorm_SeqAIJ(Mat A, NormType type, PetscReal *nrm)
2274: {
2275: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2276: const MatScalar *v;
2277: PetscReal sum = 0.0;
2278: PetscInt i, j;
2280: PetscFunctionBegin;
2281: PetscCall(MatSeqAIJGetArrayRead(A, &v));
2282: if (type == NORM_FROBENIUS) {
2283: #if defined(PETSC_USE_REAL___FP16)
2284: PetscBLASInt one = 1, nz = a->nz;
2285: PetscCallBLAS("BLASnrm2", *nrm = BLASnrm2_(&nz, v, &one));
2286: #else
2287: for (i = 0; i < a->nz; i++) {
2288: sum += PetscRealPart(PetscConj(*v) * (*v));
2289: v++;
2290: }
2291: *nrm = PetscSqrtReal(sum);
2292: #endif
2293: PetscCall(PetscLogFlops(2.0 * a->nz));
2294: } else if (type == NORM_1) {
2295: PetscReal *tmp;
2296: PetscInt *jj = a->j;
2297: PetscCall(PetscCalloc1(A->cmap->n + 1, &tmp));
2298: *nrm = 0.0;
2299: for (j = 0; j < a->nz; j++) {
2300: tmp[*jj++] += PetscAbsScalar(*v);
2301: v++;
2302: }
2303: for (j = 0; j < A->cmap->n; j++) {
2304: if (tmp[j] > *nrm) *nrm = tmp[j];
2305: }
2306: PetscCall(PetscFree(tmp));
2307: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2308: } else if (type == NORM_INFINITY) {
2309: *nrm = 0.0;
2310: for (j = 0; j < A->rmap->n; j++) {
2311: const PetscScalar *v2 = PetscSafePointerPlusOffset(v, a->i[j]);
2312: sum = 0.0;
2313: for (i = 0; i < a->i[j + 1] - a->i[j]; i++) {
2314: sum += PetscAbsScalar(*v2);
2315: v2++;
2316: }
2317: if (sum > *nrm) *nrm = sum;
2318: }
2319: PetscCall(PetscLogFlops(PetscMax(a->nz - 1, 0)));
2320: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_SUP, "No support for two norm");
2321: PetscCall(MatSeqAIJRestoreArrayRead(A, &v));
2322: PetscFunctionReturn(PETSC_SUCCESS);
2323: }
2325: static PetscErrorCode MatIsTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2326: {
2327: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2328: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2329: const MatScalar *va, *vb;
2330: PetscInt ma, na, mb, nb, i;
2332: PetscFunctionBegin;
2333: PetscCall(MatGetSize(A, &ma, &na));
2334: PetscCall(MatGetSize(B, &mb, &nb));
2335: if (ma != nb || na != mb) {
2336: *f = PETSC_FALSE;
2337: PetscFunctionReturn(PETSC_SUCCESS);
2338: }
2339: PetscCall(MatSeqAIJGetArrayRead(A, &va));
2340: PetscCall(MatSeqAIJGetArrayRead(B, &vb));
2341: aii = aij->i;
2342: bii = bij->i;
2343: adx = aij->j;
2344: bdx = bij->j;
2345: PetscCall(PetscMalloc1(ma, &aptr));
2346: PetscCall(PetscMalloc1(mb, &bptr));
2347: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2348: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2350: *f = PETSC_TRUE;
2351: for (i = 0; i < ma; i++) {
2352: while (aptr[i] < aii[i + 1]) {
2353: PetscInt idc, idr;
2354: PetscScalar vc, vr;
2355: /* column/row index/value */
2356: idc = adx[aptr[i]];
2357: idr = bdx[bptr[idc]];
2358: vc = va[aptr[i]];
2359: vr = vb[bptr[idc]];
2360: if (i != idr || PetscAbsScalar(vc - vr) > tol) {
2361: *f = PETSC_FALSE;
2362: goto done;
2363: } else {
2364: aptr[i]++;
2365: if (B || i != idc) bptr[idc]++;
2366: }
2367: }
2368: }
2369: done:
2370: PetscCall(PetscFree(aptr));
2371: PetscCall(PetscFree(bptr));
2372: PetscCall(MatSeqAIJRestoreArrayRead(A, &va));
2373: PetscCall(MatSeqAIJRestoreArrayRead(B, &vb));
2374: PetscFunctionReturn(PETSC_SUCCESS);
2375: }
2377: static PetscErrorCode MatIsHermitianTranspose_SeqAIJ(Mat A, Mat B, PetscReal tol, PetscBool *f)
2378: {
2379: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data, *bij = (Mat_SeqAIJ *)B->data;
2380: PetscInt *adx, *bdx, *aii, *bii, *aptr, *bptr;
2381: MatScalar *va, *vb;
2382: PetscInt ma, na, mb, nb, i;
2384: PetscFunctionBegin;
2385: PetscCall(MatGetSize(A, &ma, &na));
2386: PetscCall(MatGetSize(B, &mb, &nb));
2387: if (ma != nb || na != mb) {
2388: *f = PETSC_FALSE;
2389: PetscFunctionReturn(PETSC_SUCCESS);
2390: }
2391: aii = aij->i;
2392: bii = bij->i;
2393: adx = aij->j;
2394: bdx = bij->j;
2395: va = aij->a;
2396: vb = bij->a;
2397: PetscCall(PetscMalloc1(ma, &aptr));
2398: PetscCall(PetscMalloc1(mb, &bptr));
2399: for (i = 0; i < ma; i++) aptr[i] = aii[i];
2400: for (i = 0; i < mb; i++) bptr[i] = bii[i];
2402: *f = PETSC_TRUE;
2403: for (i = 0; i < ma; i++) {
2404: while (aptr[i] < aii[i + 1]) {
2405: PetscInt idc, idr;
2406: PetscScalar vc, vr;
2407: /* column/row index/value */
2408: idc = adx[aptr[i]];
2409: idr = bdx[bptr[idc]];
2410: vc = va[aptr[i]];
2411: vr = vb[bptr[idc]];
2412: if (i != idr || PetscAbsScalar(vc - PetscConj(vr)) > tol) {
2413: *f = PETSC_FALSE;
2414: goto done;
2415: } else {
2416: aptr[i]++;
2417: if (B || i != idc) bptr[idc]++;
2418: }
2419: }
2420: }
2421: done:
2422: PetscCall(PetscFree(aptr));
2423: PetscCall(PetscFree(bptr));
2424: PetscFunctionReturn(PETSC_SUCCESS);
2425: }
2427: PetscErrorCode MatDiagonalScale_SeqAIJ(Mat A, Vec ll, Vec rr)
2428: {
2429: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2430: const PetscScalar *l, *r;
2431: PetscScalar x;
2432: MatScalar *v;
2433: PetscInt i, j, m = A->rmap->n, n = A->cmap->n, M, nz = a->nz;
2434: const PetscInt *jj;
2436: PetscFunctionBegin;
2437: if (ll) {
2438: /* The local size is used so that VecMPI can be passed to this routine
2439: by MatDiagonalScale_MPIAIJ */
2440: PetscCall(VecGetLocalSize(ll, &m));
2441: PetscCheck(m == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Left scaling vector wrong length");
2442: PetscCall(VecGetArrayRead(ll, &l));
2443: PetscCall(MatSeqAIJGetArray(A, &v));
2444: for (i = 0; i < m; i++) {
2445: x = l[i];
2446: M = a->i[i + 1] - a->i[i];
2447: for (j = 0; j < M; j++) (*v++) *= x;
2448: }
2449: PetscCall(VecRestoreArrayRead(ll, &l));
2450: PetscCall(PetscLogFlops(nz));
2451: PetscCall(MatSeqAIJRestoreArray(A, &v));
2452: }
2453: if (rr) {
2454: PetscCall(VecGetLocalSize(rr, &n));
2455: PetscCheck(n == A->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Right scaling vector wrong length");
2456: PetscCall(VecGetArrayRead(rr, &r));
2457: PetscCall(MatSeqAIJGetArray(A, &v));
2458: jj = a->j;
2459: for (i = 0; i < nz; i++) (*v++) *= r[*jj++];
2460: PetscCall(MatSeqAIJRestoreArray(A, &v));
2461: PetscCall(VecRestoreArrayRead(rr, &r));
2462: PetscCall(PetscLogFlops(nz));
2463: }
2464: PetscCall(MatSeqAIJInvalidateDiagonal(A));
2465: PetscFunctionReturn(PETSC_SUCCESS);
2466: }
2468: PetscErrorCode MatCreateSubMatrix_SeqAIJ(Mat A, IS isrow, IS iscol, PetscInt csize, MatReuse scall, Mat *B)
2469: {
2470: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *c;
2471: PetscInt *smap, i, k, kstart, kend, oldcols = A->cmap->n, *lens;
2472: PetscInt row, mat_i, *mat_j, tcol, first, step, *mat_ilen, sum, lensi;
2473: const PetscInt *irow, *icol;
2474: const PetscScalar *aa;
2475: PetscInt nrows, ncols;
2476: PetscInt *starts, *j_new, *i_new, *aj = a->j, *ai = a->i, ii, *ailen = a->ilen;
2477: MatScalar *a_new, *mat_a, *c_a;
2478: Mat C;
2479: PetscBool stride;
2481: PetscFunctionBegin;
2482: PetscCall(ISGetIndices(isrow, &irow));
2483: PetscCall(ISGetLocalSize(isrow, &nrows));
2484: PetscCall(ISGetLocalSize(iscol, &ncols));
2486: PetscCall(PetscObjectTypeCompare((PetscObject)iscol, ISSTRIDE, &stride));
2487: if (stride) {
2488: PetscCall(ISStrideGetInfo(iscol, &first, &step));
2489: } else {
2490: first = 0;
2491: step = 0;
2492: }
2493: if (stride && step == 1) {
2494: /* special case of contiguous rows */
2495: PetscCall(PetscMalloc2(nrows, &lens, nrows, &starts));
2496: /* loop over new rows determining lens and starting points */
2497: for (i = 0; i < nrows; i++) {
2498: kstart = ai[irow[i]];
2499: kend = kstart + ailen[irow[i]];
2500: starts[i] = kstart;
2501: for (k = kstart; k < kend; k++) {
2502: if (aj[k] >= first) {
2503: starts[i] = k;
2504: break;
2505: }
2506: }
2507: sum = 0;
2508: while (k < kend) {
2509: if (aj[k++] >= first + ncols) break;
2510: sum++;
2511: }
2512: lens[i] = sum;
2513: }
2514: /* create submatrix */
2515: if (scall == MAT_REUSE_MATRIX) {
2516: PetscInt n_cols, n_rows;
2517: PetscCall(MatGetSize(*B, &n_rows, &n_cols));
2518: PetscCheck(n_rows == nrows && n_cols == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Reused submatrix wrong size");
2519: PetscCall(MatZeroEntries(*B));
2520: C = *B;
2521: } else {
2522: PetscInt rbs, cbs;
2523: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2524: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2525: PetscCall(ISGetBlockSize(isrow, &rbs));
2526: PetscCall(ISGetBlockSize(iscol, &cbs));
2527: PetscCall(MatSetBlockSizes(C, rbs, cbs));
2528: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2529: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2530: }
2531: c = (Mat_SeqAIJ *)C->data;
2533: /* loop over rows inserting into submatrix */
2534: PetscCall(MatSeqAIJGetArrayWrite(C, &a_new)); // Not 'a_new = c->a-new', since that raw usage ignores offload state of C
2535: j_new = c->j;
2536: i_new = c->i;
2537: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2538: for (i = 0; i < nrows; i++) {
2539: ii = starts[i];
2540: lensi = lens[i];
2541: if (lensi) {
2542: for (k = 0; k < lensi; k++) *j_new++ = aj[ii + k] - first;
2543: PetscCall(PetscArraycpy(a_new, aa + starts[i], lensi));
2544: a_new += lensi;
2545: }
2546: i_new[i + 1] = i_new[i] + lensi;
2547: c->ilen[i] = lensi;
2548: }
2549: PetscCall(MatSeqAIJRestoreArrayWrite(C, &a_new)); // Set C's offload state properly
2550: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2551: PetscCall(PetscFree2(lens, starts));
2552: } else {
2553: PetscCall(ISGetIndices(iscol, &icol));
2554: PetscCall(PetscCalloc1(oldcols, &smap));
2555: PetscCall(PetscMalloc1(1 + nrows, &lens));
2556: for (i = 0; i < ncols; i++) {
2557: PetscCheck(icol[i] < oldcols, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Requesting column beyond largest column icol[%" PetscInt_FMT "] %" PetscInt_FMT " >= A->cmap->n %" PetscInt_FMT, i, icol[i], oldcols);
2558: smap[icol[i]] = i + 1;
2559: }
2561: /* determine lens of each row */
2562: for (i = 0; i < nrows; i++) {
2563: kstart = ai[irow[i]];
2564: kend = kstart + a->ilen[irow[i]];
2565: lens[i] = 0;
2566: for (k = kstart; k < kend; k++) {
2567: if (smap[aj[k]]) lens[i]++;
2568: }
2569: }
2570: /* Create and fill new matrix */
2571: if (scall == MAT_REUSE_MATRIX) {
2572: PetscBool equal;
2574: c = (Mat_SeqAIJ *)((*B)->data);
2575: PetscCheck((*B)->rmap->n == nrows && (*B)->cmap->n == ncols, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong size");
2576: PetscCall(PetscArraycmp(c->ilen, lens, (*B)->rmap->n, &equal));
2577: PetscCheck(equal, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Cannot reuse matrix. wrong number of nonzeros");
2578: PetscCall(PetscArrayzero(c->ilen, (*B)->rmap->n));
2579: C = *B;
2580: } else {
2581: PetscInt rbs, cbs;
2582: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), &C));
2583: PetscCall(MatSetSizes(C, nrows, ncols, PETSC_DETERMINE, PETSC_DETERMINE));
2584: PetscCall(ISGetBlockSize(isrow, &rbs));
2585: PetscCall(ISGetBlockSize(iscol, &cbs));
2586: if (rbs > 1 || cbs > 1) PetscCall(MatSetBlockSizes(C, rbs, cbs));
2587: PetscCall(MatSetType(C, ((PetscObject)A)->type_name));
2588: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(C, 0, lens));
2589: }
2590: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2592: c = (Mat_SeqAIJ *)C->data;
2593: PetscCall(MatSeqAIJGetArrayWrite(C, &c_a)); // Not 'c->a', since that raw usage ignores offload state of C
2594: for (i = 0; i < nrows; i++) {
2595: row = irow[i];
2596: kstart = ai[row];
2597: kend = kstart + a->ilen[row];
2598: mat_i = c->i[i];
2599: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2600: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2601: mat_ilen = c->ilen + i;
2602: for (k = kstart; k < kend; k++) {
2603: if ((tcol = smap[a->j[k]])) {
2604: *mat_j++ = tcol - 1;
2605: *mat_a++ = aa[k];
2606: (*mat_ilen)++;
2607: }
2608: }
2609: }
2610: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2611: /* Free work space */
2612: PetscCall(ISRestoreIndices(iscol, &icol));
2613: PetscCall(PetscFree(smap));
2614: PetscCall(PetscFree(lens));
2615: /* sort */
2616: for (i = 0; i < nrows; i++) {
2617: PetscInt ilen;
2619: mat_i = c->i[i];
2620: mat_j = PetscSafePointerPlusOffset(c->j, mat_i);
2621: mat_a = PetscSafePointerPlusOffset(c_a, mat_i);
2622: ilen = c->ilen[i];
2623: PetscCall(PetscSortIntWithScalarArray(ilen, mat_j, mat_a));
2624: }
2625: PetscCall(MatSeqAIJRestoreArrayWrite(C, &c_a));
2626: }
2627: #if defined(PETSC_HAVE_DEVICE)
2628: PetscCall(MatBindToCPU(C, A->boundtocpu));
2629: #endif
2630: PetscCall(MatAssemblyBegin(C, MAT_FINAL_ASSEMBLY));
2631: PetscCall(MatAssemblyEnd(C, MAT_FINAL_ASSEMBLY));
2633: PetscCall(ISRestoreIndices(isrow, &irow));
2634: *B = C;
2635: PetscFunctionReturn(PETSC_SUCCESS);
2636: }
2638: static PetscErrorCode MatGetMultiProcBlock_SeqAIJ(Mat mat, MPI_Comm subComm, MatReuse scall, Mat *subMat)
2639: {
2640: Mat B;
2642: PetscFunctionBegin;
2643: if (scall == MAT_INITIAL_MATRIX) {
2644: PetscCall(MatCreate(subComm, &B));
2645: PetscCall(MatSetSizes(B, mat->rmap->n, mat->cmap->n, mat->rmap->n, mat->cmap->n));
2646: PetscCall(MatSetBlockSizesFromMats(B, mat, mat));
2647: PetscCall(MatSetType(B, MATSEQAIJ));
2648: PetscCall(MatDuplicateNoCreate_SeqAIJ(B, mat, MAT_COPY_VALUES, PETSC_TRUE));
2649: *subMat = B;
2650: } else {
2651: PetscCall(MatCopy_SeqAIJ(mat, *subMat, SAME_NONZERO_PATTERN));
2652: }
2653: PetscFunctionReturn(PETSC_SUCCESS);
2654: }
2656: static PetscErrorCode MatILUFactor_SeqAIJ(Mat inA, IS row, IS col, const MatFactorInfo *info)
2657: {
2658: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2659: Mat outA;
2660: PetscBool row_identity, col_identity;
2662: PetscFunctionBegin;
2663: PetscCheck(info->levels == 0, PETSC_COMM_SELF, PETSC_ERR_SUP, "Only levels=0 supported for in-place ilu");
2665: PetscCall(ISIdentity(row, &row_identity));
2666: PetscCall(ISIdentity(col, &col_identity));
2668: outA = inA;
2669: outA->factortype = MAT_FACTOR_LU;
2670: PetscCall(PetscFree(inA->solvertype));
2671: PetscCall(PetscStrallocpy(MATSOLVERPETSC, &inA->solvertype));
2673: PetscCall(PetscObjectReference((PetscObject)row));
2674: PetscCall(ISDestroy(&a->row));
2676: a->row = row;
2678: PetscCall(PetscObjectReference((PetscObject)col));
2679: PetscCall(ISDestroy(&a->col));
2681: a->col = col;
2683: /* Create the inverse permutation so that it can be used in MatLUFactorNumeric() */
2684: PetscCall(ISDestroy(&a->icol));
2685: PetscCall(ISInvertPermutation(col, PETSC_DECIDE, &a->icol));
2687: if (!a->solve_work) { /* this matrix may have been factored before */
2688: PetscCall(PetscMalloc1(inA->rmap->n + 1, &a->solve_work));
2689: }
2691: PetscCall(MatMarkDiagonal_SeqAIJ(inA));
2692: if (row_identity && col_identity) {
2693: PetscCall(MatLUFactorNumeric_SeqAIJ_inplace(outA, inA, info));
2694: } else {
2695: PetscCall(MatLUFactorNumeric_SeqAIJ_InplaceWithPerm(outA, inA, info));
2696: }
2697: PetscFunctionReturn(PETSC_SUCCESS);
2698: }
2700: PetscErrorCode MatScale_SeqAIJ(Mat inA, PetscScalar alpha)
2701: {
2702: Mat_SeqAIJ *a = (Mat_SeqAIJ *)inA->data;
2703: PetscScalar *v;
2704: PetscBLASInt one = 1, bnz;
2706: PetscFunctionBegin;
2707: PetscCall(MatSeqAIJGetArray(inA, &v));
2708: PetscCall(PetscBLASIntCast(a->nz, &bnz));
2709: PetscCallBLAS("BLASscal", BLASscal_(&bnz, &alpha, v, &one));
2710: PetscCall(PetscLogFlops(a->nz));
2711: PetscCall(MatSeqAIJRestoreArray(inA, &v));
2712: PetscCall(MatSeqAIJInvalidateDiagonal(inA));
2713: PetscFunctionReturn(PETSC_SUCCESS);
2714: }
2716: PetscErrorCode MatDestroySubMatrix_Private(Mat_SubSppt *submatj)
2717: {
2718: PetscInt i;
2720: PetscFunctionBegin;
2721: if (!submatj->id) { /* delete data that are linked only to submats[id=0] */
2722: PetscCall(PetscFree4(submatj->sbuf1, submatj->ptr, submatj->tmp, submatj->ctr));
2724: for (i = 0; i < submatj->nrqr; ++i) PetscCall(PetscFree(submatj->sbuf2[i]));
2725: PetscCall(PetscFree3(submatj->sbuf2, submatj->req_size, submatj->req_source1));
2727: if (submatj->rbuf1) {
2728: PetscCall(PetscFree(submatj->rbuf1[0]));
2729: PetscCall(PetscFree(submatj->rbuf1));
2730: }
2732: for (i = 0; i < submatj->nrqs; ++i) PetscCall(PetscFree(submatj->rbuf3[i]));
2733: PetscCall(PetscFree3(submatj->req_source2, submatj->rbuf2, submatj->rbuf3));
2734: PetscCall(PetscFree(submatj->pa));
2735: }
2737: #if defined(PETSC_USE_CTABLE)
2738: PetscCall(PetscHMapIDestroy(&submatj->rmap));
2739: if (submatj->cmap_loc) PetscCall(PetscFree(submatj->cmap_loc));
2740: PetscCall(PetscFree(submatj->rmap_loc));
2741: #else
2742: PetscCall(PetscFree(submatj->rmap));
2743: #endif
2745: if (!submatj->allcolumns) {
2746: #if defined(PETSC_USE_CTABLE)
2747: PetscCall(PetscHMapIDestroy((PetscHMapI *)&submatj->cmap));
2748: #else
2749: PetscCall(PetscFree(submatj->cmap));
2750: #endif
2751: }
2752: PetscCall(PetscFree(submatj->row2proc));
2754: PetscCall(PetscFree(submatj));
2755: PetscFunctionReturn(PETSC_SUCCESS);
2756: }
2758: PetscErrorCode MatDestroySubMatrix_SeqAIJ(Mat C)
2759: {
2760: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data;
2761: Mat_SubSppt *submatj = c->submatis1;
2763: PetscFunctionBegin;
2764: PetscCall((*submatj->destroy)(C));
2765: PetscCall(MatDestroySubMatrix_Private(submatj));
2766: PetscFunctionReturn(PETSC_SUCCESS);
2767: }
2769: /* Note this has code duplication with MatDestroySubMatrices_SeqBAIJ() */
2770: static PetscErrorCode MatDestroySubMatrices_SeqAIJ(PetscInt n, Mat *mat[])
2771: {
2772: PetscInt i;
2773: Mat C;
2774: Mat_SeqAIJ *c;
2775: Mat_SubSppt *submatj;
2777: PetscFunctionBegin;
2778: for (i = 0; i < n; i++) {
2779: C = (*mat)[i];
2780: c = (Mat_SeqAIJ *)C->data;
2781: submatj = c->submatis1;
2782: if (submatj) {
2783: if (--((PetscObject)C)->refct <= 0) {
2784: PetscCall(PetscFree(C->factorprefix));
2785: PetscCall((*submatj->destroy)(C));
2786: PetscCall(MatDestroySubMatrix_Private(submatj));
2787: PetscCall(PetscFree(C->defaultvectype));
2788: PetscCall(PetscFree(C->defaultrandtype));
2789: PetscCall(PetscLayoutDestroy(&C->rmap));
2790: PetscCall(PetscLayoutDestroy(&C->cmap));
2791: PetscCall(PetscHeaderDestroy(&C));
2792: }
2793: } else {
2794: PetscCall(MatDestroy(&C));
2795: }
2796: }
2798: /* Destroy Dummy submatrices created for reuse */
2799: PetscCall(MatDestroySubMatrices_Dummy(n, mat));
2801: PetscCall(PetscFree(*mat));
2802: PetscFunctionReturn(PETSC_SUCCESS);
2803: }
2805: static PetscErrorCode MatCreateSubMatrices_SeqAIJ(Mat A, PetscInt n, const IS irow[], const IS icol[], MatReuse scall, Mat *B[])
2806: {
2807: PetscInt i;
2809: PetscFunctionBegin;
2810: if (scall == MAT_INITIAL_MATRIX) PetscCall(PetscCalloc1(n + 1, B));
2812: for (i = 0; i < n; i++) PetscCall(MatCreateSubMatrix_SeqAIJ(A, irow[i], icol[i], PETSC_DECIDE, scall, &(*B)[i]));
2813: PetscFunctionReturn(PETSC_SUCCESS);
2814: }
2816: static PetscErrorCode MatIncreaseOverlap_SeqAIJ(Mat A, PetscInt is_max, IS is[], PetscInt ov)
2817: {
2818: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2819: PetscInt row, i, j, k, l, ll, m, n, *nidx, isz, val;
2820: const PetscInt *idx;
2821: PetscInt start, end, *ai, *aj, bs = (A->rmap->bs > 0 && A->rmap->bs == A->cmap->bs) ? A->rmap->bs : 1;
2822: PetscBT table;
2824: PetscFunctionBegin;
2825: m = A->rmap->n / bs;
2826: ai = a->i;
2827: aj = a->j;
2829: PetscCheck(ov >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "illegal negative overlap value used");
2831: PetscCall(PetscMalloc1(m + 1, &nidx));
2832: PetscCall(PetscBTCreate(m, &table));
2834: for (i = 0; i < is_max; i++) {
2835: /* Initialize the two local arrays */
2836: isz = 0;
2837: PetscCall(PetscBTMemzero(m, table));
2839: /* Extract the indices, assume there can be duplicate entries */
2840: PetscCall(ISGetIndices(is[i], &idx));
2841: PetscCall(ISGetLocalSize(is[i], &n));
2843: if (bs > 1) {
2844: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2845: for (j = 0; j < n; ++j) {
2846: if (!PetscBTLookupSet(table, idx[j] / bs)) nidx[isz++] = idx[j] / bs;
2847: }
2848: PetscCall(ISRestoreIndices(is[i], &idx));
2849: PetscCall(ISDestroy(&is[i]));
2851: k = 0;
2852: for (j = 0; j < ov; j++) { /* for each overlap */
2853: n = isz;
2854: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2855: for (ll = 0; ll < bs; ll++) {
2856: row = bs * nidx[k] + ll;
2857: start = ai[row];
2858: end = ai[row + 1];
2859: for (l = start; l < end; l++) {
2860: val = aj[l] / bs;
2861: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2862: }
2863: }
2864: }
2865: }
2866: PetscCall(ISCreateBlock(PETSC_COMM_SELF, bs, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2867: } else {
2868: /* Enter these into the temp arrays. I.e., mark table[row], enter row into new index */
2869: for (j = 0; j < n; ++j) {
2870: if (!PetscBTLookupSet(table, idx[j])) nidx[isz++] = idx[j];
2871: }
2872: PetscCall(ISRestoreIndices(is[i], &idx));
2873: PetscCall(ISDestroy(&is[i]));
2875: k = 0;
2876: for (j = 0; j < ov; j++) { /* for each overlap */
2877: n = isz;
2878: for (; k < n; k++) { /* do only those rows in nidx[k], which are not done yet */
2879: row = nidx[k];
2880: start = ai[row];
2881: end = ai[row + 1];
2882: for (l = start; l < end; l++) {
2883: val = aj[l];
2884: if (!PetscBTLookupSet(table, val)) nidx[isz++] = val;
2885: }
2886: }
2887: }
2888: PetscCall(ISCreateGeneral(PETSC_COMM_SELF, isz, nidx, PETSC_COPY_VALUES, (is + i)));
2889: }
2890: }
2891: PetscCall(PetscBTDestroy(&table));
2892: PetscCall(PetscFree(nidx));
2893: PetscFunctionReturn(PETSC_SUCCESS);
2894: }
2896: static PetscErrorCode MatPermute_SeqAIJ(Mat A, IS rowp, IS colp, Mat *B)
2897: {
2898: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2899: PetscInt i, nz = 0, m = A->rmap->n, n = A->cmap->n;
2900: const PetscInt *row, *col;
2901: PetscInt *cnew, j, *lens;
2902: IS icolp, irowp;
2903: PetscInt *cwork = NULL;
2904: PetscScalar *vwork = NULL;
2906: PetscFunctionBegin;
2907: PetscCall(ISInvertPermutation(rowp, PETSC_DECIDE, &irowp));
2908: PetscCall(ISGetIndices(irowp, &row));
2909: PetscCall(ISInvertPermutation(colp, PETSC_DECIDE, &icolp));
2910: PetscCall(ISGetIndices(icolp, &col));
2912: /* determine lengths of permuted rows */
2913: PetscCall(PetscMalloc1(m + 1, &lens));
2914: for (i = 0; i < m; i++) lens[row[i]] = a->i[i + 1] - a->i[i];
2915: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
2916: PetscCall(MatSetSizes(*B, m, n, m, n));
2917: PetscCall(MatSetBlockSizesFromMats(*B, A, A));
2918: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
2919: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*B, 0, lens));
2920: PetscCall(PetscFree(lens));
2922: PetscCall(PetscMalloc1(n, &cnew));
2923: for (i = 0; i < m; i++) {
2924: PetscCall(MatGetRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2925: for (j = 0; j < nz; j++) cnew[j] = col[cwork[j]];
2926: PetscCall(MatSetValues_SeqAIJ(*B, 1, &row[i], nz, cnew, vwork, INSERT_VALUES));
2927: PetscCall(MatRestoreRow_SeqAIJ(A, i, &nz, &cwork, &vwork));
2928: }
2929: PetscCall(PetscFree(cnew));
2931: (*B)->assembled = PETSC_FALSE;
2933: #if defined(PETSC_HAVE_DEVICE)
2934: PetscCall(MatBindToCPU(*B, A->boundtocpu));
2935: #endif
2936: PetscCall(MatAssemblyBegin(*B, MAT_FINAL_ASSEMBLY));
2937: PetscCall(MatAssemblyEnd(*B, MAT_FINAL_ASSEMBLY));
2938: PetscCall(ISRestoreIndices(irowp, &row));
2939: PetscCall(ISRestoreIndices(icolp, &col));
2940: PetscCall(ISDestroy(&irowp));
2941: PetscCall(ISDestroy(&icolp));
2942: if (rowp == colp) PetscCall(MatPropagateSymmetryOptions(A, *B));
2943: PetscFunctionReturn(PETSC_SUCCESS);
2944: }
2946: PetscErrorCode MatCopy_SeqAIJ(Mat A, Mat B, MatStructure str)
2947: {
2948: PetscFunctionBegin;
2949: /* If the two matrices have the same copy implementation, use fast copy. */
2950: if (str == SAME_NONZERO_PATTERN && (A->ops->copy == B->ops->copy)) {
2951: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2952: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
2953: const PetscScalar *aa;
2955: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
2956: PetscCheck(a->i[A->rmap->n] == b->i[B->rmap->n], PETSC_COMM_SELF, PETSC_ERR_ARG_INCOMP, "Number of nonzeros in two matrices are different %" PetscInt_FMT " != %" PetscInt_FMT, a->i[A->rmap->n], b->i[B->rmap->n]);
2957: PetscCall(PetscArraycpy(b->a, aa, a->i[A->rmap->n]));
2958: PetscCall(PetscObjectStateIncrease((PetscObject)B));
2959: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
2960: } else {
2961: PetscCall(MatCopy_Basic(A, B, str));
2962: }
2963: PetscFunctionReturn(PETSC_SUCCESS);
2964: }
2966: PETSC_INTERN PetscErrorCode MatSeqAIJGetArray_SeqAIJ(Mat A, PetscScalar *array[])
2967: {
2968: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
2970: PetscFunctionBegin;
2971: *array = a->a;
2972: PetscFunctionReturn(PETSC_SUCCESS);
2973: }
2975: PETSC_INTERN PetscErrorCode MatSeqAIJRestoreArray_SeqAIJ(Mat A, PetscScalar *array[])
2976: {
2977: PetscFunctionBegin;
2978: *array = NULL;
2979: PetscFunctionReturn(PETSC_SUCCESS);
2980: }
2982: /*
2983: Computes the number of nonzeros per row needed for preallocation when X and Y
2984: have different nonzero structure.
2985: */
2986: PetscErrorCode MatAXPYGetPreallocation_SeqX_private(PetscInt m, const PetscInt *xi, const PetscInt *xj, const PetscInt *yi, const PetscInt *yj, PetscInt *nnz)
2987: {
2988: PetscInt i, j, k, nzx, nzy;
2990: PetscFunctionBegin;
2991: /* Set the number of nonzeros in the new matrix */
2992: for (i = 0; i < m; i++) {
2993: const PetscInt *xjj = PetscSafePointerPlusOffset(xj, xi[i]), *yjj = PetscSafePointerPlusOffset(yj, yi[i]);
2994: nzx = xi[i + 1] - xi[i];
2995: nzy = yi[i + 1] - yi[i];
2996: nnz[i] = 0;
2997: for (j = 0, k = 0; j < nzx; j++) { /* Point in X */
2998: for (; k < nzy && yjj[k] < xjj[j]; k++) nnz[i]++; /* Catch up to X */
2999: if (k < nzy && yjj[k] == xjj[j]) k++; /* Skip duplicate */
3000: nnz[i]++;
3001: }
3002: for (; k < nzy; k++) nnz[i]++;
3003: }
3004: PetscFunctionReturn(PETSC_SUCCESS);
3005: }
3007: PetscErrorCode MatAXPYGetPreallocation_SeqAIJ(Mat Y, Mat X, PetscInt *nnz)
3008: {
3009: PetscInt m = Y->rmap->N;
3010: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data;
3011: Mat_SeqAIJ *y = (Mat_SeqAIJ *)Y->data;
3013: PetscFunctionBegin;
3014: /* Set the number of nonzeros in the new matrix */
3015: PetscCall(MatAXPYGetPreallocation_SeqX_private(m, x->i, x->j, y->i, y->j, nnz));
3016: PetscFunctionReturn(PETSC_SUCCESS);
3017: }
3019: PetscErrorCode MatAXPY_SeqAIJ(Mat Y, PetscScalar a, Mat X, MatStructure str)
3020: {
3021: Mat_SeqAIJ *x = (Mat_SeqAIJ *)X->data, *y = (Mat_SeqAIJ *)Y->data;
3023: PetscFunctionBegin;
3024: if (str == UNKNOWN_NONZERO_PATTERN || (PetscDefined(USE_DEBUG) && str == SAME_NONZERO_PATTERN)) {
3025: PetscBool e = x->nz == y->nz ? PETSC_TRUE : PETSC_FALSE;
3026: if (e) {
3027: PetscCall(PetscArraycmp(x->i, y->i, Y->rmap->n + 1, &e));
3028: if (e) {
3029: PetscCall(PetscArraycmp(x->j, y->j, y->nz, &e));
3030: if (e) str = SAME_NONZERO_PATTERN;
3031: }
3032: }
3033: if (!e) PetscCheck(str != SAME_NONZERO_PATTERN, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONG, "MatStructure is not SAME_NONZERO_PATTERN");
3034: }
3035: if (str == SAME_NONZERO_PATTERN) {
3036: const PetscScalar *xa;
3037: PetscScalar *ya, alpha = a;
3038: PetscBLASInt one = 1, bnz;
3040: PetscCall(PetscBLASIntCast(x->nz, &bnz));
3041: PetscCall(MatSeqAIJGetArray(Y, &ya));
3042: PetscCall(MatSeqAIJGetArrayRead(X, &xa));
3043: PetscCallBLAS("BLASaxpy", BLASaxpy_(&bnz, &alpha, xa, &one, ya, &one));
3044: PetscCall(MatSeqAIJRestoreArrayRead(X, &xa));
3045: PetscCall(MatSeqAIJRestoreArray(Y, &ya));
3046: PetscCall(PetscLogFlops(2.0 * bnz));
3047: PetscCall(MatSeqAIJInvalidateDiagonal(Y));
3048: PetscCall(PetscObjectStateIncrease((PetscObject)Y));
3049: } else if (str == SUBSET_NONZERO_PATTERN) { /* nonzeros of X is a subset of Y's */
3050: PetscCall(MatAXPY_Basic(Y, a, X, str));
3051: } else {
3052: Mat B;
3053: PetscInt *nnz;
3054: PetscCall(PetscMalloc1(Y->rmap->N, &nnz));
3055: PetscCall(MatCreate(PetscObjectComm((PetscObject)Y), &B));
3056: PetscCall(PetscObjectSetName((PetscObject)B, ((PetscObject)Y)->name));
3057: PetscCall(MatSetLayouts(B, Y->rmap, Y->cmap));
3058: PetscCall(MatSetType(B, ((PetscObject)Y)->type_name));
3059: PetscCall(MatAXPYGetPreallocation_SeqAIJ(Y, X, nnz));
3060: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
3061: PetscCall(MatAXPY_BasicWithPreallocation(B, Y, a, X, str));
3062: PetscCall(MatHeaderMerge(Y, &B));
3063: PetscCall(MatSeqAIJCheckInode(Y));
3064: PetscCall(PetscFree(nnz));
3065: }
3066: PetscFunctionReturn(PETSC_SUCCESS);
3067: }
3069: PETSC_INTERN PetscErrorCode MatConjugate_SeqAIJ(Mat mat)
3070: {
3071: #if defined(PETSC_USE_COMPLEX)
3072: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3073: PetscInt i, nz;
3074: PetscScalar *a;
3076: PetscFunctionBegin;
3077: nz = aij->nz;
3078: PetscCall(MatSeqAIJGetArray(mat, &a));
3079: for (i = 0; i < nz; i++) a[i] = PetscConj(a[i]);
3080: PetscCall(MatSeqAIJRestoreArray(mat, &a));
3081: #else
3082: PetscFunctionBegin;
3083: #endif
3084: PetscFunctionReturn(PETSC_SUCCESS);
3085: }
3087: static PetscErrorCode MatGetRowMaxAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3088: {
3089: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3090: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3091: PetscReal atmp;
3092: PetscScalar *x;
3093: const MatScalar *aa, *av;
3095: PetscFunctionBegin;
3096: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3097: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3098: aa = av;
3099: ai = a->i;
3100: aj = a->j;
3102: PetscCall(VecSet(v, 0.0));
3103: PetscCall(VecGetArrayWrite(v, &x));
3104: PetscCall(VecGetLocalSize(v, &n));
3105: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3106: for (i = 0; i < m; i++) {
3107: ncols = ai[1] - ai[0];
3108: ai++;
3109: for (j = 0; j < ncols; j++) {
3110: atmp = PetscAbsScalar(*aa);
3111: if (PetscAbsScalar(x[i]) < atmp) {
3112: x[i] = atmp;
3113: if (idx) idx[i] = *aj;
3114: }
3115: aa++;
3116: aj++;
3117: }
3118: }
3119: PetscCall(VecRestoreArrayWrite(v, &x));
3120: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3121: PetscFunctionReturn(PETSC_SUCCESS);
3122: }
3124: static PetscErrorCode MatGetRowSumAbs_SeqAIJ(Mat A, Vec v)
3125: {
3126: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3127: PetscInt i, j, m = A->rmap->n, *ai, ncols, n;
3128: PetscScalar *x;
3129: const MatScalar *aa, *av;
3131: PetscFunctionBegin;
3132: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3133: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3134: aa = av;
3135: ai = a->i;
3137: PetscCall(VecSet(v, 0.0));
3138: PetscCall(VecGetArrayWrite(v, &x));
3139: PetscCall(VecGetLocalSize(v, &n));
3140: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3141: for (i = 0; i < m; i++) {
3142: ncols = ai[1] - ai[0];
3143: ai++;
3144: for (j = 0; j < ncols; j++) {
3145: x[i] += PetscAbsScalar(*aa);
3146: aa++;
3147: }
3148: }
3149: PetscCall(VecRestoreArrayWrite(v, &x));
3150: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3151: PetscFunctionReturn(PETSC_SUCCESS);
3152: }
3154: static PetscErrorCode MatGetRowMax_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3155: {
3156: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3157: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3158: PetscScalar *x;
3159: const MatScalar *aa, *av;
3161: PetscFunctionBegin;
3162: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3163: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3164: aa = av;
3165: ai = a->i;
3166: aj = a->j;
3168: PetscCall(VecSet(v, 0.0));
3169: PetscCall(VecGetArrayWrite(v, &x));
3170: PetscCall(VecGetLocalSize(v, &n));
3171: PetscCheck(n == A->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3172: for (i = 0; i < m; i++) {
3173: ncols = ai[1] - ai[0];
3174: ai++;
3175: if (ncols == A->cmap->n) { /* row is dense */
3176: x[i] = *aa;
3177: if (idx) idx[i] = 0;
3178: } else { /* row is sparse so already KNOW maximum is 0.0 or higher */
3179: x[i] = 0.0;
3180: if (idx) {
3181: for (j = 0; j < ncols; j++) { /* find first implicit 0.0 in the row */
3182: if (aj[j] > j) {
3183: idx[i] = j;
3184: break;
3185: }
3186: }
3187: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3188: if (j == ncols && j < A->cmap->n) idx[i] = j;
3189: }
3190: }
3191: for (j = 0; j < ncols; j++) {
3192: if (PetscRealPart(x[i]) < PetscRealPart(*aa)) {
3193: x[i] = *aa;
3194: if (idx) idx[i] = *aj;
3195: }
3196: aa++;
3197: aj++;
3198: }
3199: }
3200: PetscCall(VecRestoreArrayWrite(v, &x));
3201: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3202: PetscFunctionReturn(PETSC_SUCCESS);
3203: }
3205: static PetscErrorCode MatGetRowMinAbs_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3206: {
3207: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3208: PetscInt i, j, m = A->rmap->n, *ai, *aj, ncols, n;
3209: PetscScalar *x;
3210: const MatScalar *aa, *av;
3212: PetscFunctionBegin;
3213: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3214: aa = av;
3215: ai = a->i;
3216: aj = a->j;
3218: PetscCall(VecSet(v, 0.0));
3219: PetscCall(VecGetArrayWrite(v, &x));
3220: PetscCall(VecGetLocalSize(v, &n));
3221: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector, %" PetscInt_FMT " vs. %" PetscInt_FMT " rows", m, n);
3222: for (i = 0; i < m; i++) {
3223: ncols = ai[1] - ai[0];
3224: ai++;
3225: if (ncols == A->cmap->n) { /* row is dense */
3226: x[i] = *aa;
3227: if (idx) idx[i] = 0;
3228: } else { /* row is sparse so already KNOW minimum is 0.0 or higher */
3229: x[i] = 0.0;
3230: if (idx) { /* find first implicit 0.0 in the row */
3231: for (j = 0; j < ncols; j++) {
3232: if (aj[j] > j) {
3233: idx[i] = j;
3234: break;
3235: }
3236: }
3237: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3238: if (j == ncols && j < A->cmap->n) idx[i] = j;
3239: }
3240: }
3241: for (j = 0; j < ncols; j++) {
3242: if (PetscAbsScalar(x[i]) > PetscAbsScalar(*aa)) {
3243: x[i] = *aa;
3244: if (idx) idx[i] = *aj;
3245: }
3246: aa++;
3247: aj++;
3248: }
3249: }
3250: PetscCall(VecRestoreArrayWrite(v, &x));
3251: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3252: PetscFunctionReturn(PETSC_SUCCESS);
3253: }
3255: static PetscErrorCode MatGetRowMin_SeqAIJ(Mat A, Vec v, PetscInt idx[])
3256: {
3257: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3258: PetscInt i, j, m = A->rmap->n, ncols, n;
3259: const PetscInt *ai, *aj;
3260: PetscScalar *x;
3261: const MatScalar *aa, *av;
3263: PetscFunctionBegin;
3264: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3265: PetscCall(MatSeqAIJGetArrayRead(A, &av));
3266: aa = av;
3267: ai = a->i;
3268: aj = a->j;
3270: PetscCall(VecSet(v, 0.0));
3271: PetscCall(VecGetArrayWrite(v, &x));
3272: PetscCall(VecGetLocalSize(v, &n));
3273: PetscCheck(n == m, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "Nonconforming matrix and vector");
3274: for (i = 0; i < m; i++) {
3275: ncols = ai[1] - ai[0];
3276: ai++;
3277: if (ncols == A->cmap->n) { /* row is dense */
3278: x[i] = *aa;
3279: if (idx) idx[i] = 0;
3280: } else { /* row is sparse so already KNOW minimum is 0.0 or lower */
3281: x[i] = 0.0;
3282: if (idx) { /* find first implicit 0.0 in the row */
3283: for (j = 0; j < ncols; j++) {
3284: if (aj[j] > j) {
3285: idx[i] = j;
3286: break;
3287: }
3288: }
3289: /* in case first implicit 0.0 in the row occurs at ncols-th column */
3290: if (j == ncols && j < A->cmap->n) idx[i] = j;
3291: }
3292: }
3293: for (j = 0; j < ncols; j++) {
3294: if (PetscRealPart(x[i]) > PetscRealPart(*aa)) {
3295: x[i] = *aa;
3296: if (idx) idx[i] = *aj;
3297: }
3298: aa++;
3299: aj++;
3300: }
3301: }
3302: PetscCall(VecRestoreArrayWrite(v, &x));
3303: PetscCall(MatSeqAIJRestoreArrayRead(A, &av));
3304: PetscFunctionReturn(PETSC_SUCCESS);
3305: }
3307: static PetscErrorCode MatInvertBlockDiagonal_SeqAIJ(Mat A, const PetscScalar **values)
3308: {
3309: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
3310: PetscInt i, bs = PetscAbs(A->rmap->bs), mbs = A->rmap->n / bs, ipvt[5], bs2 = bs * bs, *v_pivots, ij[7], *IJ, j;
3311: MatScalar *diag, work[25], *v_work;
3312: const PetscReal shift = 0.0;
3313: PetscBool allowzeropivot, zeropivotdetected = PETSC_FALSE;
3315: PetscFunctionBegin;
3316: allowzeropivot = PetscNot(A->erroriffailure);
3317: if (a->ibdiagvalid) {
3318: if (values) *values = a->ibdiag;
3319: PetscFunctionReturn(PETSC_SUCCESS);
3320: }
3321: PetscCall(MatMarkDiagonal_SeqAIJ(A));
3322: if (!a->ibdiag) { PetscCall(PetscMalloc1(bs2 * mbs, &a->ibdiag)); }
3323: diag = a->ibdiag;
3324: if (values) *values = a->ibdiag;
3325: /* factor and invert each block */
3326: switch (bs) {
3327: case 1:
3328: for (i = 0; i < mbs; i++) {
3329: PetscCall(MatGetValues(A, 1, &i, 1, &i, diag + i));
3330: if (PetscAbsScalar(diag[i] + shift) < PETSC_MACHINE_EPSILON) {
3331: if (allowzeropivot) {
3332: A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3333: A->factorerror_zeropivot_value = PetscAbsScalar(diag[i]);
3334: A->factorerror_zeropivot_row = i;
3335: PetscCall(PetscInfo(A, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g\n", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON));
3336: } else SETERRQ(PETSC_COMM_SELF, PETSC_ERR_MAT_LU_ZRPVT, "Zero pivot, row %" PetscInt_FMT " pivot %g tolerance %g", i, (double)PetscAbsScalar(diag[i]), (double)PETSC_MACHINE_EPSILON);
3337: }
3338: diag[i] = (PetscScalar)1.0 / (diag[i] + shift);
3339: }
3340: break;
3341: case 2:
3342: for (i = 0; i < mbs; i++) {
3343: ij[0] = 2 * i;
3344: ij[1] = 2 * i + 1;
3345: PetscCall(MatGetValues(A, 2, ij, 2, ij, diag));
3346: PetscCall(PetscKernel_A_gets_inverse_A_2(diag, shift, allowzeropivot, &zeropivotdetected));
3347: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3348: PetscCall(PetscKernel_A_gets_transpose_A_2(diag));
3349: diag += 4;
3350: }
3351: break;
3352: case 3:
3353: for (i = 0; i < mbs; i++) {
3354: ij[0] = 3 * i;
3355: ij[1] = 3 * i + 1;
3356: ij[2] = 3 * i + 2;
3357: PetscCall(MatGetValues(A, 3, ij, 3, ij, diag));
3358: PetscCall(PetscKernel_A_gets_inverse_A_3(diag, shift, allowzeropivot, &zeropivotdetected));
3359: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3360: PetscCall(PetscKernel_A_gets_transpose_A_3(diag));
3361: diag += 9;
3362: }
3363: break;
3364: case 4:
3365: for (i = 0; i < mbs; i++) {
3366: ij[0] = 4 * i;
3367: ij[1] = 4 * i + 1;
3368: ij[2] = 4 * i + 2;
3369: ij[3] = 4 * i + 3;
3370: PetscCall(MatGetValues(A, 4, ij, 4, ij, diag));
3371: PetscCall(PetscKernel_A_gets_inverse_A_4(diag, shift, allowzeropivot, &zeropivotdetected));
3372: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3373: PetscCall(PetscKernel_A_gets_transpose_A_4(diag));
3374: diag += 16;
3375: }
3376: break;
3377: case 5:
3378: for (i = 0; i < mbs; i++) {
3379: ij[0] = 5 * i;
3380: ij[1] = 5 * i + 1;
3381: ij[2] = 5 * i + 2;
3382: ij[3] = 5 * i + 3;
3383: ij[4] = 5 * i + 4;
3384: PetscCall(MatGetValues(A, 5, ij, 5, ij, diag));
3385: PetscCall(PetscKernel_A_gets_inverse_A_5(diag, ipvt, work, shift, allowzeropivot, &zeropivotdetected));
3386: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3387: PetscCall(PetscKernel_A_gets_transpose_A_5(diag));
3388: diag += 25;
3389: }
3390: break;
3391: case 6:
3392: for (i = 0; i < mbs; i++) {
3393: ij[0] = 6 * i;
3394: ij[1] = 6 * i + 1;
3395: ij[2] = 6 * i + 2;
3396: ij[3] = 6 * i + 3;
3397: ij[4] = 6 * i + 4;
3398: ij[5] = 6 * i + 5;
3399: PetscCall(MatGetValues(A, 6, ij, 6, ij, diag));
3400: PetscCall(PetscKernel_A_gets_inverse_A_6(diag, shift, allowzeropivot, &zeropivotdetected));
3401: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3402: PetscCall(PetscKernel_A_gets_transpose_A_6(diag));
3403: diag += 36;
3404: }
3405: break;
3406: case 7:
3407: for (i = 0; i < mbs; i++) {
3408: ij[0] = 7 * i;
3409: ij[1] = 7 * i + 1;
3410: ij[2] = 7 * i + 2;
3411: ij[3] = 7 * i + 3;
3412: ij[4] = 7 * i + 4;
3413: ij[5] = 7 * i + 5;
3414: ij[6] = 7 * i + 6;
3415: PetscCall(MatGetValues(A, 7, ij, 7, ij, diag));
3416: PetscCall(PetscKernel_A_gets_inverse_A_7(diag, shift, allowzeropivot, &zeropivotdetected));
3417: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3418: PetscCall(PetscKernel_A_gets_transpose_A_7(diag));
3419: diag += 49;
3420: }
3421: break;
3422: default:
3423: PetscCall(PetscMalloc3(bs, &v_work, bs, &v_pivots, bs, &IJ));
3424: for (i = 0; i < mbs; i++) {
3425: for (j = 0; j < bs; j++) IJ[j] = bs * i + j;
3426: PetscCall(MatGetValues(A, bs, IJ, bs, IJ, diag));
3427: PetscCall(PetscKernel_A_gets_inverse_A(bs, diag, v_pivots, v_work, allowzeropivot, &zeropivotdetected));
3428: if (zeropivotdetected) A->factorerrortype = MAT_FACTOR_NUMERIC_ZEROPIVOT;
3429: PetscCall(PetscKernel_A_gets_transpose_A_N(diag, bs));
3430: diag += bs2;
3431: }
3432: PetscCall(PetscFree3(v_work, v_pivots, IJ));
3433: }
3434: a->ibdiagvalid = PETSC_TRUE;
3435: PetscFunctionReturn(PETSC_SUCCESS);
3436: }
3438: static PetscErrorCode MatSetRandom_SeqAIJ(Mat x, PetscRandom rctx)
3439: {
3440: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3441: PetscScalar a, *aa;
3442: PetscInt m, n, i, j, col;
3444: PetscFunctionBegin;
3445: if (!x->assembled) {
3446: PetscCall(MatGetSize(x, &m, &n));
3447: for (i = 0; i < m; i++) {
3448: for (j = 0; j < aij->imax[i]; j++) {
3449: PetscCall(PetscRandomGetValue(rctx, &a));
3450: col = (PetscInt)(n * PetscRealPart(a));
3451: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3452: }
3453: }
3454: } else {
3455: PetscCall(MatSeqAIJGetArrayWrite(x, &aa));
3456: for (i = 0; i < aij->nz; i++) PetscCall(PetscRandomGetValue(rctx, aa + i));
3457: PetscCall(MatSeqAIJRestoreArrayWrite(x, &aa));
3458: }
3459: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3460: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3461: PetscFunctionReturn(PETSC_SUCCESS);
3462: }
3464: /* Like MatSetRandom_SeqAIJ, but do not set values on columns in range of [low, high) */
3465: PetscErrorCode MatSetRandomSkipColumnRange_SeqAIJ_Private(Mat x, PetscInt low, PetscInt high, PetscRandom rctx)
3466: {
3467: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)x->data;
3468: PetscScalar a;
3469: PetscInt m, n, i, j, col, nskip;
3471: PetscFunctionBegin;
3472: nskip = high - low;
3473: PetscCall(MatGetSize(x, &m, &n));
3474: n -= nskip; /* shrink number of columns where nonzeros can be set */
3475: for (i = 0; i < m; i++) {
3476: for (j = 0; j < aij->imax[i]; j++) {
3477: PetscCall(PetscRandomGetValue(rctx, &a));
3478: col = (PetscInt)(n * PetscRealPart(a));
3479: if (col >= low) col += nskip; /* shift col rightward to skip the hole */
3480: PetscCall(MatSetValues(x, 1, &i, 1, &col, &a, ADD_VALUES));
3481: }
3482: }
3483: PetscCall(MatAssemblyBegin(x, MAT_FINAL_ASSEMBLY));
3484: PetscCall(MatAssemblyEnd(x, MAT_FINAL_ASSEMBLY));
3485: PetscFunctionReturn(PETSC_SUCCESS);
3486: }
3488: static struct _MatOps MatOps_Values = {MatSetValues_SeqAIJ,
3489: MatGetRow_SeqAIJ,
3490: MatRestoreRow_SeqAIJ,
3491: MatMult_SeqAIJ,
3492: /* 4*/ MatMultAdd_SeqAIJ,
3493: MatMultTranspose_SeqAIJ,
3494: MatMultTransposeAdd_SeqAIJ,
3495: NULL,
3496: NULL,
3497: NULL,
3498: /* 10*/ NULL,
3499: MatLUFactor_SeqAIJ,
3500: NULL,
3501: MatSOR_SeqAIJ,
3502: MatTranspose_SeqAIJ,
3503: /*1 5*/ MatGetInfo_SeqAIJ,
3504: MatEqual_SeqAIJ,
3505: MatGetDiagonal_SeqAIJ,
3506: MatDiagonalScale_SeqAIJ,
3507: MatNorm_SeqAIJ,
3508: /* 20*/ NULL,
3509: MatAssemblyEnd_SeqAIJ,
3510: MatSetOption_SeqAIJ,
3511: MatZeroEntries_SeqAIJ,
3512: /* 24*/ MatZeroRows_SeqAIJ,
3513: NULL,
3514: NULL,
3515: NULL,
3516: NULL,
3517: /* 29*/ MatSetUp_Seq_Hash,
3518: NULL,
3519: NULL,
3520: NULL,
3521: NULL,
3522: /* 34*/ MatDuplicate_SeqAIJ,
3523: NULL,
3524: NULL,
3525: MatILUFactor_SeqAIJ,
3526: NULL,
3527: /* 39*/ MatAXPY_SeqAIJ,
3528: MatCreateSubMatrices_SeqAIJ,
3529: MatIncreaseOverlap_SeqAIJ,
3530: MatGetValues_SeqAIJ,
3531: MatCopy_SeqAIJ,
3532: /* 44*/ MatGetRowMax_SeqAIJ,
3533: MatScale_SeqAIJ,
3534: MatShift_SeqAIJ,
3535: MatDiagonalSet_SeqAIJ,
3536: MatZeroRowsColumns_SeqAIJ,
3537: /* 49*/ MatSetRandom_SeqAIJ,
3538: MatGetRowIJ_SeqAIJ,
3539: MatRestoreRowIJ_SeqAIJ,
3540: MatGetColumnIJ_SeqAIJ,
3541: MatRestoreColumnIJ_SeqAIJ,
3542: /* 54*/ MatFDColoringCreate_SeqXAIJ,
3543: NULL,
3544: NULL,
3545: MatPermute_SeqAIJ,
3546: NULL,
3547: /* 59*/ NULL,
3548: MatDestroy_SeqAIJ,
3549: MatView_SeqAIJ,
3550: NULL,
3551: NULL,
3552: /* 64*/ NULL,
3553: MatMatMatMultNumeric_SeqAIJ_SeqAIJ_SeqAIJ,
3554: NULL,
3555: NULL,
3556: NULL,
3557: /* 69*/ MatGetRowMaxAbs_SeqAIJ,
3558: MatGetRowMinAbs_SeqAIJ,
3559: NULL,
3560: NULL,
3561: NULL,
3562: /* 74*/ NULL,
3563: MatFDColoringApply_AIJ,
3564: NULL,
3565: NULL,
3566: NULL,
3567: /* 79*/ MatFindZeroDiagonals_SeqAIJ,
3568: NULL,
3569: NULL,
3570: NULL,
3571: MatLoad_SeqAIJ,
3572: /* 84*/ NULL,
3573: NULL,
3574: NULL,
3575: NULL,
3576: NULL,
3577: /* 89*/ NULL,
3578: NULL,
3579: MatMatMultNumeric_SeqAIJ_SeqAIJ,
3580: NULL,
3581: NULL,
3582: /* 94*/ MatPtAPNumeric_SeqAIJ_SeqAIJ_SparseAxpy,
3583: NULL,
3584: NULL,
3585: MatMatTransposeMultNumeric_SeqAIJ_SeqAIJ,
3586: NULL,
3587: /* 99*/ MatProductSetFromOptions_SeqAIJ,
3588: NULL,
3589: NULL,
3590: MatConjugate_SeqAIJ,
3591: NULL,
3592: /*104*/ MatSetValuesRow_SeqAIJ,
3593: MatRealPart_SeqAIJ,
3594: MatImaginaryPart_SeqAIJ,
3595: NULL,
3596: NULL,
3597: /*109*/ MatMatSolve_SeqAIJ,
3598: NULL,
3599: MatGetRowMin_SeqAIJ,
3600: NULL,
3601: MatMissingDiagonal_SeqAIJ,
3602: /*114*/ NULL,
3603: NULL,
3604: NULL,
3605: NULL,
3606: NULL,
3607: /*119*/ NULL,
3608: NULL,
3609: NULL,
3610: NULL,
3611: MatGetMultiProcBlock_SeqAIJ,
3612: /*124*/ MatFindNonzeroRows_SeqAIJ,
3613: MatGetColumnReductions_SeqAIJ,
3614: MatInvertBlockDiagonal_SeqAIJ,
3615: MatInvertVariableBlockDiagonal_SeqAIJ,
3616: NULL,
3617: /*129*/ NULL,
3618: NULL,
3619: NULL,
3620: MatTransposeMatMultNumeric_SeqAIJ_SeqAIJ,
3621: MatTransposeColoringCreate_SeqAIJ,
3622: /*134*/ MatTransColoringApplySpToDen_SeqAIJ,
3623: MatTransColoringApplyDenToSp_SeqAIJ,
3624: NULL,
3625: NULL,
3626: MatRARtNumeric_SeqAIJ_SeqAIJ,
3627: /*139*/ NULL,
3628: NULL,
3629: NULL,
3630: MatFDColoringSetUp_SeqXAIJ,
3631: MatFindOffBlockDiagonalEntries_SeqAIJ,
3632: MatCreateMPIMatConcatenateSeqMat_SeqAIJ,
3633: /*145*/ MatDestroySubMatrices_SeqAIJ,
3634: NULL,
3635: NULL,
3636: MatCreateGraph_Simple_AIJ,
3637: NULL,
3638: /*150*/ MatTransposeSymbolic_SeqAIJ,
3639: MatEliminateZeros_SeqAIJ,
3640: MatGetRowSumAbs_SeqAIJ};
3642: static PetscErrorCode MatSeqAIJSetColumnIndices_SeqAIJ(Mat mat, PetscInt *indices)
3643: {
3644: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3645: PetscInt i, nz, n;
3647: PetscFunctionBegin;
3648: nz = aij->maxnz;
3649: n = mat->rmap->n;
3650: for (i = 0; i < nz; i++) aij->j[i] = indices[i];
3651: aij->nz = nz;
3652: for (i = 0; i < n; i++) aij->ilen[i] = aij->imax[i];
3653: PetscFunctionReturn(PETSC_SUCCESS);
3654: }
3656: /*
3657: * Given a sparse matrix with global column indices, compact it by using a local column space.
3658: * The result matrix helps saving memory in other algorithms, such as MatPtAPSymbolic_MPIAIJ_MPIAIJ_scalable()
3659: */
3660: PetscErrorCode MatSeqAIJCompactOutExtraColumns_SeqAIJ(Mat mat, ISLocalToGlobalMapping *mapping)
3661: {
3662: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3663: PetscHMapI gid1_lid1;
3664: PetscHashIter tpos;
3665: PetscInt gid, lid, i, ec, nz = aij->nz;
3666: PetscInt *garray, *jj = aij->j;
3668: PetscFunctionBegin;
3670: PetscAssertPointer(mapping, 2);
3671: /* use a table */
3672: PetscCall(PetscHMapICreateWithSize(mat->rmap->n, &gid1_lid1));
3673: ec = 0;
3674: for (i = 0; i < nz; i++) {
3675: PetscInt data, gid1 = jj[i] + 1;
3676: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &data));
3677: if (!data) {
3678: /* one based table */
3679: PetscCall(PetscHMapISet(gid1_lid1, gid1, ++ec));
3680: }
3681: }
3682: /* form array of columns we need */
3683: PetscCall(PetscMalloc1(ec, &garray));
3684: PetscHashIterBegin(gid1_lid1, tpos);
3685: while (!PetscHashIterAtEnd(gid1_lid1, tpos)) {
3686: PetscHashIterGetKey(gid1_lid1, tpos, gid);
3687: PetscHashIterGetVal(gid1_lid1, tpos, lid);
3688: PetscHashIterNext(gid1_lid1, tpos);
3689: gid--;
3690: lid--;
3691: garray[lid] = gid;
3692: }
3693: PetscCall(PetscSortInt(ec, garray)); /* sort, and rebuild */
3694: PetscCall(PetscHMapIClear(gid1_lid1));
3695: for (i = 0; i < ec; i++) PetscCall(PetscHMapISet(gid1_lid1, garray[i] + 1, i + 1));
3696: /* compact out the extra columns in B */
3697: for (i = 0; i < nz; i++) {
3698: PetscInt gid1 = jj[i] + 1;
3699: PetscCall(PetscHMapIGetWithDefault(gid1_lid1, gid1, 0, &lid));
3700: lid--;
3701: jj[i] = lid;
3702: }
3703: PetscCall(PetscLayoutDestroy(&mat->cmap));
3704: PetscCall(PetscHMapIDestroy(&gid1_lid1));
3705: PetscCall(PetscLayoutCreateFromSizes(PetscObjectComm((PetscObject)mat), ec, ec, 1, &mat->cmap));
3706: PetscCall(ISLocalToGlobalMappingCreate(PETSC_COMM_SELF, mat->cmap->bs, mat->cmap->n, garray, PETSC_OWN_POINTER, mapping));
3707: PetscCall(ISLocalToGlobalMappingSetType(*mapping, ISLOCALTOGLOBALMAPPINGHASH));
3708: PetscFunctionReturn(PETSC_SUCCESS);
3709: }
3711: /*@
3712: MatSeqAIJSetColumnIndices - Set the column indices for all the rows
3713: in the matrix.
3715: Input Parameters:
3716: + mat - the `MATSEQAIJ` matrix
3717: - indices - the column indices
3719: Level: advanced
3721: Notes:
3722: This can be called if you have precomputed the nonzero structure of the
3723: matrix and want to provide it to the matrix object to improve the performance
3724: of the `MatSetValues()` operation.
3726: You MUST have set the correct numbers of nonzeros per row in the call to
3727: `MatCreateSeqAIJ()`, and the columns indices MUST be sorted.
3729: MUST be called before any calls to `MatSetValues()`
3731: The indices should start with zero, not one.
3733: .seealso: [](ch_matrices), `Mat`, `MATSEQAIJ`
3734: @*/
3735: PetscErrorCode MatSeqAIJSetColumnIndices(Mat mat, PetscInt *indices)
3736: {
3737: PetscFunctionBegin;
3739: PetscAssertPointer(indices, 2);
3740: PetscUseMethod(mat, "MatSeqAIJSetColumnIndices_C", (Mat, PetscInt *), (mat, indices));
3741: PetscFunctionReturn(PETSC_SUCCESS);
3742: }
3744: static PetscErrorCode MatStoreValues_SeqAIJ(Mat mat)
3745: {
3746: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3747: size_t nz = aij->i[mat->rmap->n];
3749: PetscFunctionBegin;
3750: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3752: /* allocate space for values if not already there */
3753: if (!aij->saved_values) { PetscCall(PetscMalloc1(nz + 1, &aij->saved_values)); }
3755: /* copy values over */
3756: PetscCall(PetscArraycpy(aij->saved_values, aij->a, nz));
3757: PetscFunctionReturn(PETSC_SUCCESS);
3758: }
3760: /*@
3761: MatStoreValues - Stashes a copy of the matrix values; this allows reusing of the linear part of a Jacobian, while recomputing only the
3762: nonlinear portion.
3764: Logically Collect
3766: Input Parameter:
3767: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3769: Level: advanced
3771: Example Usage:
3772: .vb
3773: Using SNES
3774: Create Jacobian matrix
3775: Set linear terms into matrix
3776: Apply boundary conditions to matrix, at this time matrix must have
3777: final nonzero structure (i.e. setting the nonlinear terms and applying
3778: boundary conditions again will not change the nonzero structure
3779: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3780: MatStoreValues(mat);
3781: Call SNESSetJacobian() with matrix
3782: In your Jacobian routine
3783: MatRetrieveValues(mat);
3784: Set nonlinear terms in matrix
3786: Without `SNESSolve()`, i.e. when you handle nonlinear solve yourself:
3787: // build linear portion of Jacobian
3788: MatSetOption(mat, MAT_NEW_NONZERO_LOCATIONS, PETSC_FALSE);
3789: MatStoreValues(mat);
3790: loop over nonlinear iterations
3791: MatRetrieveValues(mat);
3792: // call MatSetValues(mat,...) to set nonliner portion of Jacobian
3793: // call MatAssemblyBegin/End() on matrix
3794: Solve linear system with Jacobian
3795: endloop
3796: .ve
3798: Notes:
3799: Matrix must already be assembled before calling this routine
3800: Must set the matrix option `MatSetOption`(mat,`MAT_NEW_NONZERO_LOCATIONS`,`PETSC_FALSE`); before
3801: calling this routine.
3803: When this is called multiple times it overwrites the previous set of stored values
3804: and does not allocated additional space.
3806: .seealso: [](ch_matrices), `Mat`, `MatRetrieveValues()`
3807: @*/
3808: PetscErrorCode MatStoreValues(Mat mat)
3809: {
3810: PetscFunctionBegin;
3812: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3813: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3814: PetscUseMethod(mat, "MatStoreValues_C", (Mat), (mat));
3815: PetscFunctionReturn(PETSC_SUCCESS);
3816: }
3818: static PetscErrorCode MatRetrieveValues_SeqAIJ(Mat mat)
3819: {
3820: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
3821: PetscInt nz = aij->i[mat->rmap->n];
3823: PetscFunctionBegin;
3824: PetscCheck(aij->nonew, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatSetOption(A,MAT_NEW_NONZERO_LOCATIONS,PETSC_FALSE);first");
3825: PetscCheck(aij->saved_values, PETSC_COMM_SELF, PETSC_ERR_ORDER, "Must call MatStoreValues(A);first");
3826: /* copy values over */
3827: PetscCall(PetscArraycpy(aij->a, aij->saved_values, nz));
3828: PetscFunctionReturn(PETSC_SUCCESS);
3829: }
3831: /*@
3832: MatRetrieveValues - Retrieves the copy of the matrix values that was stored with `MatStoreValues()`
3834: Logically Collect
3836: Input Parameter:
3837: . mat - the matrix (currently only `MATAIJ` matrices support this option)
3839: Level: advanced
3841: .seealso: [](ch_matrices), `Mat`, `MatStoreValues()`
3842: @*/
3843: PetscErrorCode MatRetrieveValues(Mat mat)
3844: {
3845: PetscFunctionBegin;
3847: PetscCheck(mat->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
3848: PetscCheck(!mat->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
3849: PetscUseMethod(mat, "MatRetrieveValues_C", (Mat), (mat));
3850: PetscFunctionReturn(PETSC_SUCCESS);
3851: }
3853: /*@C
3854: MatCreateSeqAIJ - Creates a sparse matrix in `MATSEQAIJ` (compressed row) format
3855: (the default parallel PETSc format). For good matrix assembly performance
3856: the user should preallocate the matrix storage by setting the parameter `nz`
3857: (or the array `nnz`).
3859: Collective
3861: Input Parameters:
3862: + comm - MPI communicator, set to `PETSC_COMM_SELF`
3863: . m - number of rows
3864: . n - number of columns
3865: . nz - number of nonzeros per row (same for all rows)
3866: - nnz - array containing the number of nonzeros in the various rows
3867: (possibly different for each row) or NULL
3869: Output Parameter:
3870: . A - the matrix
3872: Options Database Keys:
3873: + -mat_no_inode - Do not use inodes
3874: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3876: Level: intermediate
3878: Notes:
3879: It is recommend to use `MatCreateFromOptions()` instead of this routine
3881: If `nnz` is given then `nz` is ignored
3883: The `MATSEQAIJ` format, also called
3884: compressed row storage, is fully compatible with standard Fortran
3885: storage. That is, the stored row and column indices can begin at
3886: either one (as in Fortran) or zero.
3888: Specify the preallocated storage with either `nz` or `nnz` (not both).
3889: Set `nz` = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3890: allocation.
3892: By default, this format uses inodes (identical nodes) when possible, to
3893: improve numerical efficiency of matrix-vector products and solves. We
3894: search for consecutive rows with the same nonzero structure, thereby
3895: reusing matrix information to achieve increased efficiency.
3897: .seealso: [](ch_matrices), `Mat`, [Sparse Matrix Creation](sec_matsparse), `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`
3898: @*/
3899: PetscErrorCode MatCreateSeqAIJ(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt nz, const PetscInt nnz[], Mat *A)
3900: {
3901: PetscFunctionBegin;
3902: PetscCall(MatCreate(comm, A));
3903: PetscCall(MatSetSizes(*A, m, n, m, n));
3904: PetscCall(MatSetType(*A, MATSEQAIJ));
3905: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*A, nz, nnz));
3906: PetscFunctionReturn(PETSC_SUCCESS);
3907: }
3909: /*@C
3910: MatSeqAIJSetPreallocation - For good matrix assembly performance
3911: the user should preallocate the matrix storage by setting the parameter nz
3912: (or the array nnz). By setting these parameters accurately, performance
3913: during matrix assembly can be increased by more than a factor of 50.
3915: Collective
3917: Input Parameters:
3918: + B - The matrix
3919: . nz - number of nonzeros per row (same for all rows)
3920: - nnz - array containing the number of nonzeros in the various rows
3921: (possibly different for each row) or NULL
3923: Options Database Keys:
3924: + -mat_no_inode - Do not use inodes
3925: - -mat_inode_limit <limit> - Sets inode limit (max limit=5)
3927: Level: intermediate
3929: Notes:
3930: If `nnz` is given then `nz` is ignored
3932: The `MATSEQAIJ` format also called
3933: compressed row storage, is fully compatible with standard Fortran
3934: storage. That is, the stored row and column indices can begin at
3935: either one (as in Fortran) or zero. See the users' manual for details.
3937: Specify the preallocated storage with either `nz` or `nnz` (not both).
3938: Set nz = `PETSC_DEFAULT` and `nnz` = `NULL` for PETSc to control dynamic memory
3939: allocation.
3941: You can call `MatGetInfo()` to get information on how effective the preallocation was;
3942: for example the fields mallocs,nz_allocated,nz_used,nz_unneeded;
3943: You can also run with the option -info and look for messages with the string
3944: malloc in them to see if additional memory allocation was needed.
3946: Developer Notes:
3947: Use nz of `MAT_SKIP_ALLOCATION` to not allocate any space for the matrix
3948: entries or columns indices
3950: By default, this format uses inodes (identical nodes) when possible, to
3951: improve numerical efficiency of matrix-vector products and solves. We
3952: search for consecutive rows with the same nonzero structure, thereby
3953: reusing matrix information to achieve increased efficiency.
3955: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatSetValues()`, `MatSeqAIJSetColumnIndices()`, `MatCreateSeqAIJWithArrays()`, `MatGetInfo()`,
3956: `MatSeqAIJSetTotalPreallocation()`
3957: @*/
3958: PetscErrorCode MatSeqAIJSetPreallocation(Mat B, PetscInt nz, const PetscInt nnz[])
3959: {
3960: PetscFunctionBegin;
3963: PetscTryMethod(B, "MatSeqAIJSetPreallocation_C", (Mat, PetscInt, const PetscInt[]), (B, nz, nnz));
3964: PetscFunctionReturn(PETSC_SUCCESS);
3965: }
3967: PetscErrorCode MatSeqAIJSetPreallocation_SeqAIJ(Mat B, PetscInt nz, const PetscInt *nnz)
3968: {
3969: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
3970: PetscBool skipallocation = PETSC_FALSE, realalloc = PETSC_FALSE;
3971: PetscInt i;
3973: PetscFunctionBegin;
3974: if (B->hash_active) {
3975: B->ops[0] = b->cops;
3976: PetscCall(PetscHMapIJVDestroy(&b->ht));
3977: PetscCall(PetscFree(b->dnz));
3978: B->hash_active = PETSC_FALSE;
3979: }
3980: if (nz >= 0 || nnz) realalloc = PETSC_TRUE;
3981: if (nz == MAT_SKIP_ALLOCATION) {
3982: skipallocation = PETSC_TRUE;
3983: nz = 0;
3984: }
3985: PetscCall(PetscLayoutSetUp(B->rmap));
3986: PetscCall(PetscLayoutSetUp(B->cmap));
3988: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 5;
3989: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nz cannot be less than 0: value %" PetscInt_FMT, nz);
3990: if (PetscUnlikelyDebug(nnz)) {
3991: for (i = 0; i < B->rmap->n; i++) {
3992: PetscCheck(nnz[i] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be less than 0: local row %" PetscInt_FMT " value %" PetscInt_FMT, i, nnz[i]);
3993: PetscCheck(nnz[i] <= B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "nnz cannot be greater than row length: local row %" PetscInt_FMT " value %" PetscInt_FMT " rowlength %" PetscInt_FMT, i, nnz[i], B->cmap->n);
3994: }
3995: }
3997: B->preallocated = PETSC_TRUE;
3998: if (!skipallocation) {
3999: if (!b->imax) { PetscCall(PetscMalloc1(B->rmap->n, &b->imax)); }
4000: if (!b->ilen) {
4001: /* b->ilen will count nonzeros in each row so far. */
4002: PetscCall(PetscCalloc1(B->rmap->n, &b->ilen));
4003: } else {
4004: PetscCall(PetscMemzero(b->ilen, B->rmap->n * sizeof(PetscInt)));
4005: }
4006: if (!b->ipre) PetscCall(PetscMalloc1(B->rmap->n, &b->ipre));
4007: if (!nnz) {
4008: if (nz == PETSC_DEFAULT || nz == PETSC_DECIDE) nz = 10;
4009: else if (nz < 0) nz = 1;
4010: nz = PetscMin(nz, B->cmap->n);
4011: for (i = 0; i < B->rmap->n; i++) b->imax[i] = nz;
4012: PetscCall(PetscIntMultError(nz, B->rmap->n, &nz));
4013: } else {
4014: PetscInt64 nz64 = 0;
4015: for (i = 0; i < B->rmap->n; i++) {
4016: b->imax[i] = nnz[i];
4017: nz64 += nnz[i];
4018: }
4019: PetscCall(PetscIntCast(nz64, &nz));
4020: }
4022: /* allocate the matrix space */
4023: /* FIXME: should B's old memory be unlogged? */
4024: PetscCall(MatSeqXAIJFreeAIJ(B, &b->a, &b->j, &b->i));
4025: if (B->structure_only) {
4026: PetscCall(PetscMalloc1(nz, &b->j));
4027: PetscCall(PetscMalloc1(B->rmap->n + 1, &b->i));
4028: } else {
4029: PetscCall(PetscMalloc3(nz, &b->a, nz, &b->j, B->rmap->n + 1, &b->i));
4030: }
4031: b->i[0] = 0;
4032: for (i = 1; i < B->rmap->n + 1; i++) b->i[i] = b->i[i - 1] + b->imax[i - 1];
4033: if (B->structure_only) {
4034: b->singlemalloc = PETSC_FALSE;
4035: b->free_a = PETSC_FALSE;
4036: } else {
4037: b->singlemalloc = PETSC_TRUE;
4038: b->free_a = PETSC_TRUE;
4039: }
4040: b->free_ij = PETSC_TRUE;
4041: } else {
4042: b->free_a = PETSC_FALSE;
4043: b->free_ij = PETSC_FALSE;
4044: }
4046: if (b->ipre && nnz != b->ipre && b->imax) {
4047: /* reserve user-requested sparsity */
4048: PetscCall(PetscArraycpy(b->ipre, b->imax, B->rmap->n));
4049: }
4051: b->nz = 0;
4052: b->maxnz = nz;
4053: B->info.nz_unneeded = (double)b->maxnz;
4054: if (realalloc) PetscCall(MatSetOption(B, MAT_NEW_NONZERO_ALLOCATION_ERR, PETSC_TRUE));
4055: B->was_assembled = PETSC_FALSE;
4056: B->assembled = PETSC_FALSE;
4057: /* We simply deem preallocation has changed nonzero state. Updating the state
4058: will give clients (like AIJKokkos) a chance to know something has happened.
4059: */
4060: B->nonzerostate++;
4061: PetscFunctionReturn(PETSC_SUCCESS);
4062: }
4064: static PetscErrorCode MatResetPreallocation_SeqAIJ(Mat A)
4065: {
4066: Mat_SeqAIJ *a;
4067: PetscInt i;
4068: PetscBool skipreset;
4070: PetscFunctionBegin;
4073: /* Check local size. If zero, then return */
4074: if (!A->rmap->n) PetscFunctionReturn(PETSC_SUCCESS);
4076: a = (Mat_SeqAIJ *)A->data;
4077: /* if no saved info, we error out */
4078: PetscCheck(a->ipre, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "No saved preallocation info ");
4080: PetscCheck(a->i && a->imax && a->ilen, PETSC_COMM_SELF, PETSC_ERR_ARG_NULL, "Memory info is incomplete, and can not reset preallocation ");
4082: PetscCall(PetscArraycmp(a->ipre, a->ilen, A->rmap->n, &skipreset));
4083: if (!skipreset) {
4084: PetscCall(PetscArraycpy(a->imax, a->ipre, A->rmap->n));
4085: PetscCall(PetscArrayzero(a->ilen, A->rmap->n));
4086: a->i[0] = 0;
4087: for (i = 1; i < A->rmap->n + 1; i++) a->i[i] = a->i[i - 1] + a->imax[i - 1];
4088: A->preallocated = PETSC_TRUE;
4089: a->nz = 0;
4090: a->maxnz = a->i[A->rmap->n];
4091: A->info.nz_unneeded = (double)a->maxnz;
4092: A->was_assembled = PETSC_FALSE;
4093: A->assembled = PETSC_FALSE;
4094: }
4095: PetscFunctionReturn(PETSC_SUCCESS);
4096: }
4098: /*@
4099: MatSeqAIJSetPreallocationCSR - Allocates memory for a sparse sequential matrix in `MATSEQAIJ` format.
4101: Input Parameters:
4102: + B - the matrix
4103: . i - the indices into `j` for the start of each row (indices start with zero)
4104: . j - the column indices for each row (indices start with zero) these must be sorted for each row
4105: - v - optional values in the matrix, use `NULL` if not provided
4107: Level: developer
4109: Notes:
4110: The `i`,`j`,`v` values are COPIED with this routine; to avoid the copy use `MatCreateSeqAIJWithArrays()`
4112: This routine may be called multiple times with different nonzero patterns (or the same nonzero pattern). The nonzero
4113: structure will be the union of all the previous nonzero structures.
4115: Developer Notes:
4116: An optimization could be added to the implementation where it checks if the `i`, and `j` are identical to the current `i` and `j` and
4117: then just copies the `v` values directly with `PetscMemcpy()`.
4119: This routine could also take a `PetscCopyMode` argument to allow sharing the values instead of always copying them.
4121: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateSeqAIJ()`, `MatSetValues()`, `MatSeqAIJSetPreallocation()`, `MATSEQAIJ`, `MatResetPreallocation()`
4122: @*/
4123: PetscErrorCode MatSeqAIJSetPreallocationCSR(Mat B, const PetscInt i[], const PetscInt j[], const PetscScalar v[])
4124: {
4125: PetscFunctionBegin;
4128: PetscTryMethod(B, "MatSeqAIJSetPreallocationCSR_C", (Mat, const PetscInt[], const PetscInt[], const PetscScalar[]), (B, i, j, v));
4129: PetscFunctionReturn(PETSC_SUCCESS);
4130: }
4132: static PetscErrorCode MatSeqAIJSetPreallocationCSR_SeqAIJ(Mat B, const PetscInt Ii[], const PetscInt J[], const PetscScalar v[])
4133: {
4134: PetscInt i;
4135: PetscInt m, n;
4136: PetscInt nz;
4137: PetscInt *nnz;
4139: PetscFunctionBegin;
4140: PetscCheck(Ii[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Ii[0] must be 0 it is %" PetscInt_FMT, Ii[0]);
4142: PetscCall(PetscLayoutSetUp(B->rmap));
4143: PetscCall(PetscLayoutSetUp(B->cmap));
4145: PetscCall(MatGetSize(B, &m, &n));
4146: PetscCall(PetscMalloc1(m + 1, &nnz));
4147: for (i = 0; i < m; i++) {
4148: nz = Ii[i + 1] - Ii[i];
4149: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Local row %" PetscInt_FMT " has a negative number of columns %" PetscInt_FMT, i, nz);
4150: nnz[i] = nz;
4151: }
4152: PetscCall(MatSeqAIJSetPreallocation(B, 0, nnz));
4153: PetscCall(PetscFree(nnz));
4155: for (i = 0; i < m; i++) PetscCall(MatSetValues_SeqAIJ(B, 1, &i, Ii[i + 1] - Ii[i], J + Ii[i], PetscSafePointerPlusOffset(v, Ii[i]), INSERT_VALUES));
4157: PetscCall(MatAssemblyBegin(B, MAT_FINAL_ASSEMBLY));
4158: PetscCall(MatAssemblyEnd(B, MAT_FINAL_ASSEMBLY));
4160: PetscCall(MatSetOption(B, MAT_NEW_NONZERO_LOCATION_ERR, PETSC_TRUE));
4161: PetscFunctionReturn(PETSC_SUCCESS);
4162: }
4164: /*@
4165: MatSeqAIJKron - Computes `C`, the Kronecker product of `A` and `B`.
4167: Input Parameters:
4168: + A - left-hand side matrix
4169: . B - right-hand side matrix
4170: - reuse - either `MAT_INITIAL_MATRIX` or `MAT_REUSE_MATRIX`
4172: Output Parameter:
4173: . C - Kronecker product of `A` and `B`
4175: Level: intermediate
4177: Note:
4178: `MAT_REUSE_MATRIX` can only be used when the nonzero structure of the product matrix has not changed from that last call to `MatSeqAIJKron()`.
4180: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATKAIJ`, `MatReuse`
4181: @*/
4182: PetscErrorCode MatSeqAIJKron(Mat A, Mat B, MatReuse reuse, Mat *C)
4183: {
4184: PetscFunctionBegin;
4189: PetscAssertPointer(C, 4);
4190: if (reuse == MAT_REUSE_MATRIX) {
4193: }
4194: PetscTryMethod(A, "MatSeqAIJKron_C", (Mat, Mat, MatReuse, Mat *), (A, B, reuse, C));
4195: PetscFunctionReturn(PETSC_SUCCESS);
4196: }
4198: static PetscErrorCode MatSeqAIJKron_SeqAIJ(Mat A, Mat B, MatReuse reuse, Mat *C)
4199: {
4200: Mat newmat;
4201: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
4202: Mat_SeqAIJ *b = (Mat_SeqAIJ *)B->data;
4203: PetscScalar *v;
4204: const PetscScalar *aa, *ba;
4205: PetscInt *i, *j, m, n, p, q, nnz = 0, am = A->rmap->n, bm = B->rmap->n, an = A->cmap->n, bn = B->cmap->n;
4206: PetscBool flg;
4208: PetscFunctionBegin;
4209: PetscCheck(!A->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4210: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4211: PetscCheck(!B->factortype, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for factored matrix");
4212: PetscCheck(B->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Not for unassembled matrix");
4213: PetscCall(PetscObjectTypeCompare((PetscObject)B, MATSEQAIJ, &flg));
4214: PetscCheck(flg, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatType %s", ((PetscObject)B)->type_name);
4215: PetscCheck(reuse == MAT_INITIAL_MATRIX || reuse == MAT_REUSE_MATRIX, PETSC_COMM_SELF, PETSC_ERR_SUP, "MatReuse %d", (int)reuse);
4216: if (reuse == MAT_INITIAL_MATRIX) {
4217: PetscCall(PetscMalloc2(am * bm + 1, &i, a->i[am] * b->i[bm], &j));
4218: PetscCall(MatCreate(PETSC_COMM_SELF, &newmat));
4219: PetscCall(MatSetSizes(newmat, am * bm, an * bn, am * bm, an * bn));
4220: PetscCall(MatSetType(newmat, MATAIJ));
4221: i[0] = 0;
4222: for (m = 0; m < am; ++m) {
4223: for (p = 0; p < bm; ++p) {
4224: i[m * bm + p + 1] = i[m * bm + p] + (a->i[m + 1] - a->i[m]) * (b->i[p + 1] - b->i[p]);
4225: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4226: for (q = b->i[p]; q < b->i[p + 1]; ++q) j[nnz++] = a->j[n] * bn + b->j[q];
4227: }
4228: }
4229: }
4230: PetscCall(MatSeqAIJSetPreallocationCSR(newmat, i, j, NULL));
4231: *C = newmat;
4232: PetscCall(PetscFree2(i, j));
4233: nnz = 0;
4234: }
4235: PetscCall(MatSeqAIJGetArray(*C, &v));
4236: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
4237: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
4238: for (m = 0; m < am; ++m) {
4239: for (p = 0; p < bm; ++p) {
4240: for (n = a->i[m]; n < a->i[m + 1]; ++n) {
4241: for (q = b->i[p]; q < b->i[p + 1]; ++q) v[nnz++] = aa[n] * ba[q];
4242: }
4243: }
4244: }
4245: PetscCall(MatSeqAIJRestoreArray(*C, &v));
4246: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
4247: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
4248: PetscFunctionReturn(PETSC_SUCCESS);
4249: }
4251: #include <../src/mat/impls/dense/seq/dense.h>
4252: #include <petsc/private/kernels/petscaxpy.h>
4254: /*
4255: Computes (B'*A')' since computing B*A directly is untenable
4257: n p p
4258: [ ] [ ] [ ]
4259: m [ A ] * n [ B ] = m [ C ]
4260: [ ] [ ] [ ]
4262: */
4263: PetscErrorCode MatMatMultNumeric_SeqDense_SeqAIJ(Mat A, Mat B, Mat C)
4264: {
4265: Mat_SeqDense *sub_a = (Mat_SeqDense *)A->data;
4266: Mat_SeqAIJ *sub_b = (Mat_SeqAIJ *)B->data;
4267: Mat_SeqDense *sub_c = (Mat_SeqDense *)C->data;
4268: PetscInt i, j, n, m, q, p;
4269: const PetscInt *ii, *idx;
4270: const PetscScalar *b, *a, *a_q;
4271: PetscScalar *c, *c_q;
4272: PetscInt clda = sub_c->lda;
4273: PetscInt alda = sub_a->lda;
4275: PetscFunctionBegin;
4276: m = A->rmap->n;
4277: n = A->cmap->n;
4278: p = B->cmap->n;
4279: a = sub_a->v;
4280: b = sub_b->a;
4281: c = sub_c->v;
4282: if (clda == m) {
4283: PetscCall(PetscArrayzero(c, m * p));
4284: } else {
4285: for (j = 0; j < p; j++)
4286: for (i = 0; i < m; i++) c[j * clda + i] = 0.0;
4287: }
4288: ii = sub_b->i;
4289: idx = sub_b->j;
4290: for (i = 0; i < n; i++) {
4291: q = ii[i + 1] - ii[i];
4292: while (q-- > 0) {
4293: c_q = c + clda * (*idx);
4294: a_q = a + alda * i;
4295: PetscKernelAXPY(c_q, *b, a_q, m);
4296: idx++;
4297: b++;
4298: }
4299: }
4300: PetscFunctionReturn(PETSC_SUCCESS);
4301: }
4303: PetscErrorCode MatMatMultSymbolic_SeqDense_SeqAIJ(Mat A, Mat B, PetscReal fill, Mat C)
4304: {
4305: PetscInt m = A->rmap->n, n = B->cmap->n;
4306: PetscBool cisdense;
4308: PetscFunctionBegin;
4309: PetscCheck(A->cmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_ARG_SIZ, "A->cmap->n %" PetscInt_FMT " != B->rmap->n %" PetscInt_FMT, A->cmap->n, B->rmap->n);
4310: PetscCall(MatSetSizes(C, m, n, m, n));
4311: PetscCall(MatSetBlockSizesFromMats(C, A, B));
4312: PetscCall(PetscObjectTypeCompareAny((PetscObject)C, &cisdense, MATSEQDENSE, MATSEQDENSECUDA, MATSEQDENSEHIP, ""));
4313: if (!cisdense) PetscCall(MatSetType(C, MATDENSE));
4314: PetscCall(MatSetUp(C));
4316: C->ops->matmultnumeric = MatMatMultNumeric_SeqDense_SeqAIJ;
4317: PetscFunctionReturn(PETSC_SUCCESS);
4318: }
4320: /*MC
4321: MATSEQAIJ - MATSEQAIJ = "seqaij" - A matrix type to be used for sequential sparse matrices,
4322: based on compressed sparse row format.
4324: Options Database Key:
4325: . -mat_type seqaij - sets the matrix type to "seqaij" during a call to MatSetFromOptions()
4327: Level: beginner
4329: Notes:
4330: `MatSetValues()` may be called for this matrix type with a `NULL` argument for the numerical values,
4331: in this case the values associated with the rows and columns one passes in are set to zero
4332: in the matrix
4334: `MatSetOptions`(,`MAT_STRUCTURE_ONLY`,`PETSC_TRUE`) may be called for this matrix type. In this no
4335: space is allocated for the nonzero entries and any entries passed with `MatSetValues()` are ignored
4337: Developer Note:
4338: It would be nice if all matrix formats supported passing `NULL` in for the numerical values
4340: .seealso: [](ch_matrices), `Mat`, `MatCreateSeqAIJ()`, `MatSetFromOptions()`, `MatSetType()`, `MatCreate()`, `MatType`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4341: M*/
4343: /*MC
4344: MATAIJ - MATAIJ = "aij" - A matrix type to be used for sparse matrices.
4346: This matrix type is identical to `MATSEQAIJ` when constructed with a single process communicator,
4347: and `MATMPIAIJ` otherwise. As a result, for single process communicators,
4348: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4349: for communicators controlling multiple processes. It is recommended that you call both of
4350: the above preallocation routines for simplicity.
4352: Options Database Key:
4353: . -mat_type aij - sets the matrix type to "aij" during a call to `MatSetFromOptions()`
4355: Level: beginner
4357: Note:
4358: Subclasses include `MATAIJCUSPARSE`, `MATAIJPERM`, `MATAIJSELL`, `MATAIJMKL`, `MATAIJCRL`, and also automatically switches over to use inodes when
4359: enough exist.
4361: .seealso: [](ch_matrices), `Mat`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MATSEQAIJ`, `MATMPIAIJ`, `MATSELL`, `MATSEQSELL`, `MATMPISELL`
4362: M*/
4364: /*MC
4365: MATAIJCRL - MATAIJCRL = "aijcrl" - A matrix type to be used for sparse matrices.
4367: Options Database Key:
4368: . -mat_type aijcrl - sets the matrix type to "aijcrl" during a call to `MatSetFromOptions()`
4370: Level: beginner
4372: Note:
4373: This matrix type is identical to `MATSEQAIJCRL` when constructed with a single process communicator,
4374: and `MATMPIAIJCRL` otherwise. As a result, for single process communicators,
4375: `MatSeqAIJSetPreallocation()` is supported, and similarly `MatMPIAIJSetPreallocation()` is supported
4376: for communicators controlling multiple processes. It is recommended that you call both of
4377: the above preallocation routines for simplicity.
4379: .seealso: [](ch_matrices), `Mat`, `MatCreateMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`, `MATSEQAIJCRL`, `MATMPIAIJCRL`
4380: M*/
4382: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCRL(Mat, MatType, MatReuse, Mat *);
4383: #if defined(PETSC_HAVE_ELEMENTAL)
4384: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_Elemental(Mat, MatType, MatReuse, Mat *);
4385: #endif
4386: #if defined(PETSC_HAVE_SCALAPACK)
4387: PETSC_INTERN PetscErrorCode MatConvert_AIJ_ScaLAPACK(Mat, MatType, MatReuse, Mat *);
4388: #endif
4389: #if defined(PETSC_HAVE_HYPRE)
4390: PETSC_INTERN PetscErrorCode MatConvert_AIJ_HYPRE(Mat A, MatType, MatReuse, Mat *);
4391: #endif
4393: PETSC_EXTERN PetscErrorCode MatConvert_SeqAIJ_SeqSELL(Mat, MatType, MatReuse, Mat *);
4394: PETSC_INTERN PetscErrorCode MatConvert_XAIJ_IS(Mat, MatType, MatReuse, Mat *);
4395: PETSC_INTERN PetscErrorCode MatProductSetFromOptions_IS_XAIJ(Mat);
4397: /*@C
4398: MatSeqAIJGetArray - gives read/write access to the array where the data for a `MATSEQAIJ` matrix is stored
4400: Not Collective
4402: Input Parameter:
4403: . A - a `MATSEQAIJ` matrix
4405: Output Parameter:
4406: . array - pointer to the data
4408: Level: intermediate
4410: Fortran Notes:
4411: `MatSeqAIJGetArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJGetArrayF90()`
4413: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4414: @*/
4415: PetscErrorCode MatSeqAIJGetArray(Mat A, PetscScalar **array)
4416: {
4417: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4419: PetscFunctionBegin;
4420: if (aij->ops->getarray) {
4421: PetscCall((*aij->ops->getarray)(A, array));
4422: } else {
4423: *array = aij->a;
4424: }
4425: PetscFunctionReturn(PETSC_SUCCESS);
4426: }
4428: /*@C
4429: MatSeqAIJRestoreArray - returns access to the array where the data for a `MATSEQAIJ` matrix is stored obtained by `MatSeqAIJGetArray()`
4431: Not Collective
4433: Input Parameters:
4434: + A - a `MATSEQAIJ` matrix
4435: - array - pointer to the data
4437: Level: intermediate
4439: Fortran Notes:
4440: `MatSeqAIJRestoreArray()` Fortran binding is deprecated (since PETSc 3.19), use `MatSeqAIJRestoreArrayF90()`
4442: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayF90()`
4443: @*/
4444: PetscErrorCode MatSeqAIJRestoreArray(Mat A, PetscScalar **array)
4445: {
4446: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4448: PetscFunctionBegin;
4449: if (aij->ops->restorearray) {
4450: PetscCall((*aij->ops->restorearray)(A, array));
4451: } else {
4452: *array = NULL;
4453: }
4454: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4455: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4456: PetscFunctionReturn(PETSC_SUCCESS);
4457: }
4459: /*@C
4460: MatSeqAIJGetArrayRead - gives read-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4462: Not Collective; No Fortran Support
4464: Input Parameter:
4465: . A - a `MATSEQAIJ` matrix
4467: Output Parameter:
4468: . array - pointer to the data
4470: Level: intermediate
4472: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4473: @*/
4474: PetscErrorCode MatSeqAIJGetArrayRead(Mat A, const PetscScalar **array)
4475: {
4476: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4478: PetscFunctionBegin;
4479: if (aij->ops->getarrayread) {
4480: PetscCall((*aij->ops->getarrayread)(A, array));
4481: } else {
4482: *array = aij->a;
4483: }
4484: PetscFunctionReturn(PETSC_SUCCESS);
4485: }
4487: /*@C
4488: MatSeqAIJRestoreArrayRead - restore the read-only access array obtained from `MatSeqAIJGetArrayRead()`
4490: Not Collective; No Fortran Support
4492: Input Parameter:
4493: . A - a `MATSEQAIJ` matrix
4495: Output Parameter:
4496: . array - pointer to the data
4498: Level: intermediate
4500: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4501: @*/
4502: PetscErrorCode MatSeqAIJRestoreArrayRead(Mat A, const PetscScalar **array)
4503: {
4504: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4506: PetscFunctionBegin;
4507: if (aij->ops->restorearrayread) {
4508: PetscCall((*aij->ops->restorearrayread)(A, array));
4509: } else {
4510: *array = NULL;
4511: }
4512: PetscFunctionReturn(PETSC_SUCCESS);
4513: }
4515: /*@C
4516: MatSeqAIJGetArrayWrite - gives write-only access to the array where the data for a `MATSEQAIJ` matrix is stored
4518: Not Collective; No Fortran Support
4520: Input Parameter:
4521: . A - a `MATSEQAIJ` matrix
4523: Output Parameter:
4524: . array - pointer to the data
4526: Level: intermediate
4528: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJRestoreArrayRead()`
4529: @*/
4530: PetscErrorCode MatSeqAIJGetArrayWrite(Mat A, PetscScalar **array)
4531: {
4532: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4534: PetscFunctionBegin;
4535: if (aij->ops->getarraywrite) {
4536: PetscCall((*aij->ops->getarraywrite)(A, array));
4537: } else {
4538: *array = aij->a;
4539: }
4540: PetscCall(MatSeqAIJInvalidateDiagonal(A));
4541: PetscCall(PetscObjectStateIncrease((PetscObject)A));
4542: PetscFunctionReturn(PETSC_SUCCESS);
4543: }
4545: /*@C
4546: MatSeqAIJRestoreArrayWrite - restore the read-only access array obtained from MatSeqAIJGetArrayRead
4548: Not Collective; No Fortran Support
4550: Input Parameter:
4551: . A - a MATSEQAIJ matrix
4553: Output Parameter:
4554: . array - pointer to the data
4556: Level: intermediate
4558: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4559: @*/
4560: PetscErrorCode MatSeqAIJRestoreArrayWrite(Mat A, PetscScalar **array)
4561: {
4562: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4564: PetscFunctionBegin;
4565: if (aij->ops->restorearraywrite) {
4566: PetscCall((*aij->ops->restorearraywrite)(A, array));
4567: } else {
4568: *array = NULL;
4569: }
4570: PetscFunctionReturn(PETSC_SUCCESS);
4571: }
4573: /*@C
4574: MatSeqAIJGetCSRAndMemType - Get the CSR arrays and the memory type of the `MATSEQAIJ` matrix
4576: Not Collective; No Fortran Support
4578: Input Parameter:
4579: . mat - a matrix of type `MATSEQAIJ` or its subclasses
4581: Output Parameters:
4582: + i - row map array of the matrix
4583: . j - column index array of the matrix
4584: . a - data array of the matrix
4585: - mtype - memory type of the arrays
4587: Level: developer
4589: Notes:
4590: Any of the output parameters can be `NULL`, in which case the corresponding value is not returned.
4591: If mat is a device matrix, the arrays are on the device. Otherwise, they are on the host.
4593: One can call this routine on a preallocated but not assembled matrix to just get the memory of the CSR underneath the matrix.
4594: If the matrix is assembled, the data array `a` is guaranteed to have the latest values of the matrix.
4596: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJGetArray()`, `MatSeqAIJGetArrayRead()`
4597: @*/
4598: PetscErrorCode MatSeqAIJGetCSRAndMemType(Mat mat, const PetscInt **i, const PetscInt **j, PetscScalar **a, PetscMemType *mtype)
4599: {
4600: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)mat->data;
4602: PetscFunctionBegin;
4603: PetscCheck(mat->preallocated, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "matrix is not preallocated");
4604: if (aij->ops->getcsrandmemtype) {
4605: PetscCall((*aij->ops->getcsrandmemtype)(mat, i, j, a, mtype));
4606: } else {
4607: if (i) *i = aij->i;
4608: if (j) *j = aij->j;
4609: if (a) *a = aij->a;
4610: if (mtype) *mtype = PETSC_MEMTYPE_HOST;
4611: }
4612: PetscFunctionReturn(PETSC_SUCCESS);
4613: }
4615: /*@C
4616: MatSeqAIJGetMaxRowNonzeros - returns the maximum number of nonzeros in any row
4618: Not Collective
4620: Input Parameter:
4621: . A - a `MATSEQAIJ` matrix
4623: Output Parameter:
4624: . nz - the maximum number of nonzeros in any row
4626: Level: intermediate
4628: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRestoreArray()`, `MatSeqAIJGetArrayF90()`
4629: @*/
4630: PetscErrorCode MatSeqAIJGetMaxRowNonzeros(Mat A, PetscInt *nz)
4631: {
4632: Mat_SeqAIJ *aij = (Mat_SeqAIJ *)A->data;
4634: PetscFunctionBegin;
4635: *nz = aij->rmax;
4636: PetscFunctionReturn(PETSC_SUCCESS);
4637: }
4639: static PetscErrorCode MatCOOStructDestroy_SeqAIJ(void *data)
4640: {
4641: MatCOOStruct_SeqAIJ *coo = (MatCOOStruct_SeqAIJ *)data;
4643: PetscFunctionBegin;
4644: PetscCall(PetscFree(coo->perm));
4645: PetscCall(PetscFree(coo->jmap));
4646: PetscCall(PetscFree(coo));
4647: PetscFunctionReturn(PETSC_SUCCESS);
4648: }
4650: PetscErrorCode MatSetPreallocationCOO_SeqAIJ(Mat mat, PetscCount coo_n, PetscInt coo_i[], PetscInt coo_j[])
4651: {
4652: MPI_Comm comm;
4653: PetscInt *i, *j;
4654: PetscInt M, N, row, iprev;
4655: PetscCount k, p, q, nneg, nnz, start, end; /* Index the coo array, so use PetscCount as their type */
4656: PetscInt *Ai; /* Change to PetscCount once we use it for row pointers */
4657: PetscInt *Aj;
4658: PetscScalar *Aa;
4659: Mat_SeqAIJ *seqaij = (Mat_SeqAIJ *)mat->data;
4660: MatType rtype;
4661: PetscCount *perm, *jmap;
4662: PetscContainer container;
4663: MatCOOStruct_SeqAIJ *coo;
4664: PetscBool isorted;
4666: PetscFunctionBegin;
4667: PetscCall(PetscObjectGetComm((PetscObject)mat, &comm));
4668: PetscCall(MatGetSize(mat, &M, &N));
4669: i = coo_i;
4670: j = coo_j;
4671: PetscCall(PetscMalloc1(coo_n, &perm));
4673: /* Ignore entries with negative row or col indices; at the same time, check if i[] is already sorted (e.g., MatConvert_AlJ_HYPRE results in this case) */
4674: isorted = PETSC_TRUE;
4675: iprev = PETSC_INT_MIN;
4676: for (k = 0; k < coo_n; k++) {
4677: if (j[k] < 0) i[k] = -1;
4678: if (isorted) {
4679: if (i[k] < iprev) isorted = PETSC_FALSE;
4680: else iprev = i[k];
4681: }
4682: perm[k] = k;
4683: }
4685: /* Sort by row if not already */
4686: if (!isorted) PetscCall(PetscSortIntWithIntCountArrayPair(coo_n, i, j, perm));
4688: /* Advance k to the first row with a non-negative index */
4689: for (k = 0; k < coo_n; k++)
4690: if (i[k] >= 0) break;
4691: nneg = k;
4692: PetscCall(PetscMalloc1(coo_n - nneg + 1, &jmap)); /* +1 to make a CSR-like data structure. jmap[i] originally is the number of repeats for i-th nonzero */
4693: nnz = 0; /* Total number of unique nonzeros to be counted */
4694: jmap++; /* Inc jmap by 1 for convenience */
4696: PetscCall(PetscCalloc1(M + 1, &Ai)); /* CSR of A */
4697: PetscCall(PetscMalloc1(coo_n - nneg, &Aj)); /* We have at most coo_n-nneg unique nonzeros */
4699: /* Support for HYPRE */
4700: PetscBool hypre;
4701: const char *name;
4702: PetscCall(PetscObjectGetName((PetscObject)mat, &name));
4703: PetscCall(PetscStrcmp("_internal_COO_mat_for_hypre", name, &hypre));
4705: /* In each row, sort by column, then unique column indices to get row length */
4706: Ai++; /* Inc by 1 for convenience */
4707: q = 0; /* q-th unique nonzero, with q starting from 0 */
4708: while (k < coo_n) {
4709: PetscBool strictly_sorted; // this row is strictly sorted?
4710: PetscInt jprev;
4712: /* get [start,end) indices for this row; also check if cols in this row are strictly sorted */
4713: row = i[k];
4714: start = k;
4715: jprev = PETSC_INT_MIN;
4716: strictly_sorted = PETSC_TRUE;
4717: while (k < coo_n && i[k] == row) {
4718: if (strictly_sorted) {
4719: if (j[k] <= jprev) strictly_sorted = PETSC_FALSE;
4720: else jprev = j[k];
4721: }
4722: k++;
4723: }
4724: end = k;
4726: /* hack for HYPRE: swap min column to diag so that diagonal values will go first */
4727: if (hypre) {
4728: PetscInt minj = PETSC_MAX_INT;
4729: PetscBool hasdiag = PETSC_FALSE;
4731: if (strictly_sorted) { // fast path to swap the first and the diag
4732: PetscCount tmp;
4733: for (p = start; p < end; p++) {
4734: if (j[p] == row && p != start) {
4735: j[p] = j[start];
4736: j[start] = row;
4737: tmp = perm[start];
4738: perm[start] = perm[p];
4739: perm[p] = tmp;
4740: break;
4741: }
4742: }
4743: } else {
4744: for (p = start; p < end; p++) {
4745: hasdiag = (PetscBool)(hasdiag || (j[p] == row));
4746: minj = PetscMin(minj, j[p]);
4747: }
4749: if (hasdiag) {
4750: for (p = start; p < end; p++) {
4751: if (j[p] == minj) j[p] = row;
4752: else if (j[p] == row) j[p] = minj;
4753: }
4754: }
4755: }
4756: }
4757: // sort by columns in a row
4758: if (!strictly_sorted) PetscCall(PetscSortIntWithCountArray(end - start, j + start, perm + start));
4760: if (strictly_sorted) { // fast path to set Aj[], jmap[], Ai[], nnz, q
4761: for (p = start; p < end; p++, q++) {
4762: Aj[q] = j[p];
4763: jmap[q] = 1;
4764: }
4765: Ai[row] = end - start;
4766: nnz += Ai[row]; // q is already advanced
4767: } else {
4768: /* Find number of unique col entries in this row */
4769: Aj[q] = j[start]; /* Log the first nonzero in this row */
4770: jmap[q] = 1; /* Number of repeats of this nonzero entry */
4771: Ai[row] = 1;
4772: nnz++;
4774: for (p = start + 1; p < end; p++) { /* Scan remaining nonzero in this row */
4775: if (j[p] != j[p - 1]) { /* Meet a new nonzero */
4776: q++;
4777: jmap[q] = 1;
4778: Aj[q] = j[p];
4779: Ai[row]++;
4780: nnz++;
4781: } else {
4782: jmap[q]++;
4783: }
4784: }
4785: q++; /* Move to next row and thus next unique nonzero */
4786: }
4787: }
4789: Ai--; /* Back to the beginning of Ai[] */
4790: for (k = 0; k < M; k++) Ai[k + 1] += Ai[k];
4791: jmap--; // Back to the beginning of jmap[]
4792: jmap[0] = 0;
4793: for (k = 0; k < nnz; k++) jmap[k + 1] += jmap[k];
4795: if (nnz < coo_n - nneg) { /* Realloc with actual number of unique nonzeros */
4796: PetscCount *jmap_new;
4797: PetscInt *Aj_new;
4799: PetscCall(PetscMalloc1(nnz + 1, &jmap_new));
4800: PetscCall(PetscArraycpy(jmap_new, jmap, nnz + 1));
4801: PetscCall(PetscFree(jmap));
4802: jmap = jmap_new;
4804: PetscCall(PetscMalloc1(nnz, &Aj_new));
4805: PetscCall(PetscArraycpy(Aj_new, Aj, nnz));
4806: PetscCall(PetscFree(Aj));
4807: Aj = Aj_new;
4808: }
4810: if (nneg) { /* Discard heading entries with negative indices in perm[], as we'll access it from index 0 in MatSetValuesCOO */
4811: PetscCount *perm_new;
4813: PetscCall(PetscMalloc1(coo_n - nneg, &perm_new));
4814: PetscCall(PetscArraycpy(perm_new, perm + nneg, coo_n - nneg));
4815: PetscCall(PetscFree(perm));
4816: perm = perm_new;
4817: }
4819: PetscCall(MatGetRootType_Private(mat, &rtype));
4820: PetscCall(PetscCalloc1(nnz, &Aa)); /* Zero the matrix */
4821: PetscCall(MatSetSeqAIJWithArrays_private(PETSC_COMM_SELF, M, N, Ai, Aj, Aa, rtype, mat));
4823: seqaij->singlemalloc = PETSC_FALSE; /* Ai, Aj and Aa are not allocated in one big malloc */
4824: seqaij->free_a = seqaij->free_ij = PETSC_TRUE; /* Let newmat own Ai, Aj and Aa */
4826: // Put the COO struct in a container and then attach that to the matrix
4827: PetscCall(PetscMalloc1(1, &coo));
4828: coo->nz = nnz;
4829: coo->n = coo_n;
4830: coo->Atot = coo_n - nneg; // Annz is seqaij->nz, so no need to record that again
4831: coo->jmap = jmap; // of length nnz+1
4832: coo->perm = perm;
4833: PetscCall(PetscContainerCreate(PETSC_COMM_SELF, &container));
4834: PetscCall(PetscContainerSetPointer(container, coo));
4835: PetscCall(PetscContainerSetUserDestroy(container, MatCOOStructDestroy_SeqAIJ));
4836: PetscCall(PetscObjectCompose((PetscObject)mat, "__PETSc_MatCOOStruct_Host", (PetscObject)container));
4837: PetscCall(PetscContainerDestroy(&container));
4838: PetscFunctionReturn(PETSC_SUCCESS);
4839: }
4841: static PetscErrorCode MatSetValuesCOO_SeqAIJ(Mat A, const PetscScalar v[], InsertMode imode)
4842: {
4843: Mat_SeqAIJ *aseq = (Mat_SeqAIJ *)A->data;
4844: PetscCount i, j, Annz = aseq->nz;
4845: PetscCount *perm, *jmap;
4846: PetscScalar *Aa;
4847: PetscContainer container;
4848: MatCOOStruct_SeqAIJ *coo;
4850: PetscFunctionBegin;
4851: PetscCall(PetscObjectQuery((PetscObject)A, "__PETSc_MatCOOStruct_Host", (PetscObject *)&container));
4852: PetscCheck(container, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Not found MatCOOStruct on this matrix");
4853: PetscCall(PetscContainerGetPointer(container, (void **)&coo));
4854: perm = coo->perm;
4855: jmap = coo->jmap;
4856: PetscCall(MatSeqAIJGetArray(A, &Aa));
4857: for (i = 0; i < Annz; i++) {
4858: PetscScalar sum = 0.0;
4859: for (j = jmap[i]; j < jmap[i + 1]; j++) sum += v[perm[j]];
4860: Aa[i] = (imode == INSERT_VALUES ? 0.0 : Aa[i]) + sum;
4861: }
4862: PetscCall(MatSeqAIJRestoreArray(A, &Aa));
4863: PetscFunctionReturn(PETSC_SUCCESS);
4864: }
4866: #if defined(PETSC_HAVE_CUDA)
4867: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJCUSPARSE(Mat, MatType, MatReuse, Mat *);
4868: #endif
4869: #if defined(PETSC_HAVE_HIP)
4870: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJHIPSPARSE(Mat, MatType, MatReuse, Mat *);
4871: #endif
4872: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4873: PETSC_INTERN PetscErrorCode MatConvert_SeqAIJ_SeqAIJKokkos(Mat, MatType, MatReuse, Mat *);
4874: #endif
4876: PETSC_EXTERN PetscErrorCode MatCreate_SeqAIJ(Mat B)
4877: {
4878: Mat_SeqAIJ *b;
4879: PetscMPIInt size;
4881: PetscFunctionBegin;
4882: PetscCallMPI(MPI_Comm_size(PetscObjectComm((PetscObject)B), &size));
4883: PetscCheck(size <= 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Comm must be of size 1");
4885: PetscCall(PetscNew(&b));
4887: B->data = (void *)b;
4888: B->ops[0] = MatOps_Values;
4889: if (B->sortedfull) B->ops->setvalues = MatSetValues_SeqAIJ_SortedFull;
4891: b->row = NULL;
4892: b->col = NULL;
4893: b->icol = NULL;
4894: b->reallocs = 0;
4895: b->ignorezeroentries = PETSC_FALSE;
4896: b->roworiented = PETSC_TRUE;
4897: b->nonew = 0;
4898: b->diag = NULL;
4899: b->solve_work = NULL;
4900: B->spptr = NULL;
4901: b->saved_values = NULL;
4902: b->idiag = NULL;
4903: b->mdiag = NULL;
4904: b->ssor_work = NULL;
4905: b->omega = 1.0;
4906: b->fshift = 0.0;
4907: b->idiagvalid = PETSC_FALSE;
4908: b->ibdiagvalid = PETSC_FALSE;
4909: b->keepnonzeropattern = PETSC_FALSE;
4911: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4912: #if defined(PETSC_HAVE_MATLAB)
4913: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEnginePut_C", MatlabEnginePut_SeqAIJ));
4914: PetscCall(PetscObjectComposeFunction((PetscObject)B, "PetscMatlabEngineGet_C", MatlabEngineGet_SeqAIJ));
4915: #endif
4916: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetColumnIndices_C", MatSeqAIJSetColumnIndices_SeqAIJ));
4917: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatStoreValues_C", MatStoreValues_SeqAIJ));
4918: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatRetrieveValues_C", MatRetrieveValues_SeqAIJ));
4919: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsbaij_C", MatConvert_SeqAIJ_SeqSBAIJ));
4920: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqbaij_C", MatConvert_SeqAIJ_SeqBAIJ));
4921: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijperm_C", MatConvert_SeqAIJ_SeqAIJPERM));
4922: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijsell_C", MatConvert_SeqAIJ_SeqAIJSELL));
4923: #if defined(PETSC_HAVE_MKL_SPARSE)
4924: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijmkl_C", MatConvert_SeqAIJ_SeqAIJMKL));
4925: #endif
4926: #if defined(PETSC_HAVE_CUDA)
4927: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcusparse_C", MatConvert_SeqAIJ_SeqAIJCUSPARSE));
4928: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijcusparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4929: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijcusparse_C", MatProductSetFromOptions_SeqAIJ));
4930: #endif
4931: #if defined(PETSC_HAVE_HIP)
4932: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijhipsparse_C", MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
4933: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaijhipsparse_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4934: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaijhipsparse_C", MatProductSetFromOptions_SeqAIJ));
4935: #endif
4936: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
4937: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijkokkos_C", MatConvert_SeqAIJ_SeqAIJKokkos));
4938: #endif
4939: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqaijcrl_C", MatConvert_SeqAIJ_SeqAIJCRL));
4940: #if defined(PETSC_HAVE_ELEMENTAL)
4941: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_elemental_C", MatConvert_SeqAIJ_Elemental));
4942: #endif
4943: #if defined(PETSC_HAVE_SCALAPACK)
4944: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_scalapack_C", MatConvert_AIJ_ScaLAPACK));
4945: #endif
4946: #if defined(PETSC_HAVE_HYPRE)
4947: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_hypre_C", MatConvert_AIJ_HYPRE));
4948: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_transpose_seqaij_seqaij_C", MatProductSetFromOptions_Transpose_AIJ_AIJ));
4949: #endif
4950: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqdense_C", MatConvert_SeqAIJ_SeqDense));
4951: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_seqsell_C", MatConvert_SeqAIJ_SeqSELL));
4952: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatConvert_seqaij_is_C", MatConvert_XAIJ_IS));
4953: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsTranspose_C", MatIsTranspose_SeqAIJ));
4954: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatIsHermitianTranspose_C", MatIsHermitianTranspose_SeqAIJ));
4955: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocation_C", MatSeqAIJSetPreallocation_SeqAIJ));
4956: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatResetPreallocation_C", MatResetPreallocation_SeqAIJ));
4957: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJSetPreallocationCSR_C", MatSeqAIJSetPreallocationCSR_SeqAIJ));
4958: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatReorderForNonzeroDiagonal_C", MatReorderForNonzeroDiagonal_SeqAIJ));
4959: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_is_seqaij_C", MatProductSetFromOptions_IS_XAIJ));
4960: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqdense_seqaij_C", MatProductSetFromOptions_SeqDense_SeqAIJ));
4961: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatProductSetFromOptions_seqaij_seqaij_C", MatProductSetFromOptions_SeqAIJ));
4962: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSeqAIJKron_C", MatSeqAIJKron_SeqAIJ));
4963: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetPreallocationCOO_C", MatSetPreallocationCOO_SeqAIJ));
4964: PetscCall(PetscObjectComposeFunction((PetscObject)B, "MatSetValuesCOO_C", MatSetValuesCOO_SeqAIJ));
4965: PetscCall(MatCreate_SeqAIJ_Inode(B));
4966: PetscCall(PetscObjectChangeTypeName((PetscObject)B, MATSEQAIJ));
4967: PetscCall(MatSeqAIJSetTypeFromOptions(B)); /* this allows changing the matrix subtype to say MATSEQAIJPERM */
4968: PetscFunctionReturn(PETSC_SUCCESS);
4969: }
4971: /*
4972: Given a matrix generated with MatGetFactor() duplicates all the information in A into C
4973: */
4974: PetscErrorCode MatDuplicateNoCreate_SeqAIJ(Mat C, Mat A, MatDuplicateOption cpvalues, PetscBool mallocmatspace)
4975: {
4976: Mat_SeqAIJ *c = (Mat_SeqAIJ *)C->data, *a = (Mat_SeqAIJ *)A->data;
4977: PetscInt m = A->rmap->n, i;
4979: PetscFunctionBegin;
4980: PetscCheck(A->assembled || cpvalues == MAT_DO_NOT_COPY_VALUES, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot duplicate unassembled matrix");
4982: C->factortype = A->factortype;
4983: c->row = NULL;
4984: c->col = NULL;
4985: c->icol = NULL;
4986: c->reallocs = 0;
4987: c->diagonaldense = a->diagonaldense;
4989: C->assembled = A->assembled;
4991: if (A->preallocated) {
4992: PetscCall(PetscLayoutReference(A->rmap, &C->rmap));
4993: PetscCall(PetscLayoutReference(A->cmap, &C->cmap));
4995: if (!A->hash_active) {
4996: PetscCall(PetscMalloc1(m, &c->imax));
4997: PetscCall(PetscMemcpy(c->imax, a->imax, m * sizeof(PetscInt)));
4998: PetscCall(PetscMalloc1(m, &c->ilen));
4999: PetscCall(PetscMemcpy(c->ilen, a->ilen, m * sizeof(PetscInt)));
5001: /* allocate the matrix space */
5002: if (mallocmatspace) {
5003: PetscCall(PetscMalloc3(a->i[m], &c->a, a->i[m], &c->j, m + 1, &c->i));
5005: c->singlemalloc = PETSC_TRUE;
5007: PetscCall(PetscArraycpy(c->i, a->i, m + 1));
5008: if (m > 0) {
5009: PetscCall(PetscArraycpy(c->j, a->j, a->i[m]));
5010: if (cpvalues == MAT_COPY_VALUES) {
5011: const PetscScalar *aa;
5013: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5014: PetscCall(PetscArraycpy(c->a, aa, a->i[m]));
5015: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5016: } else {
5017: PetscCall(PetscArrayzero(c->a, a->i[m]));
5018: }
5019: }
5020: }
5021: C->preallocated = PETSC_TRUE;
5022: } else {
5023: PetscCheck(mallocmatspace, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_WRONGSTATE, "Cannot malloc matrix memory from a non-preallocated matrix");
5024: PetscCall(MatSetUp(C));
5025: }
5027: c->ignorezeroentries = a->ignorezeroentries;
5028: c->roworiented = a->roworiented;
5029: c->nonew = a->nonew;
5030: if (a->diag) {
5031: PetscCall(PetscMalloc1(m + 1, &c->diag));
5032: PetscCall(PetscMemcpy(c->diag, a->diag, m * sizeof(PetscInt)));
5033: } else c->diag = NULL;
5035: c->solve_work = NULL;
5036: c->saved_values = NULL;
5037: c->idiag = NULL;
5038: c->ssor_work = NULL;
5039: c->keepnonzeropattern = a->keepnonzeropattern;
5040: c->free_a = PETSC_TRUE;
5041: c->free_ij = PETSC_TRUE;
5043: c->rmax = a->rmax;
5044: c->nz = a->nz;
5045: c->maxnz = a->nz; /* Since we allocate exactly the right amount */
5047: c->compressedrow.use = a->compressedrow.use;
5048: c->compressedrow.nrows = a->compressedrow.nrows;
5049: if (a->compressedrow.use) {
5050: i = a->compressedrow.nrows;
5051: PetscCall(PetscMalloc2(i + 1, &c->compressedrow.i, i, &c->compressedrow.rindex));
5052: PetscCall(PetscArraycpy(c->compressedrow.i, a->compressedrow.i, i + 1));
5053: PetscCall(PetscArraycpy(c->compressedrow.rindex, a->compressedrow.rindex, i));
5054: } else {
5055: c->compressedrow.use = PETSC_FALSE;
5056: c->compressedrow.i = NULL;
5057: c->compressedrow.rindex = NULL;
5058: }
5059: c->nonzerorowcnt = a->nonzerorowcnt;
5060: C->nonzerostate = A->nonzerostate;
5062: PetscCall(MatDuplicate_SeqAIJ_Inode(A, cpvalues, &C));
5063: }
5064: PetscCall(PetscFunctionListDuplicate(((PetscObject)A)->qlist, &((PetscObject)C)->qlist));
5065: PetscFunctionReturn(PETSC_SUCCESS);
5066: }
5068: PetscErrorCode MatDuplicate_SeqAIJ(Mat A, MatDuplicateOption cpvalues, Mat *B)
5069: {
5070: PetscFunctionBegin;
5071: PetscCall(MatCreate(PetscObjectComm((PetscObject)A), B));
5072: PetscCall(MatSetSizes(*B, A->rmap->n, A->cmap->n, A->rmap->n, A->cmap->n));
5073: if (!(A->rmap->n % A->rmap->bs) && !(A->cmap->n % A->cmap->bs)) PetscCall(MatSetBlockSizesFromMats(*B, A, A));
5074: PetscCall(MatSetType(*B, ((PetscObject)A)->type_name));
5075: PetscCall(MatDuplicateNoCreate_SeqAIJ(*B, A, cpvalues, PETSC_TRUE));
5076: PetscFunctionReturn(PETSC_SUCCESS);
5077: }
5079: PetscErrorCode MatLoad_SeqAIJ(Mat newMat, PetscViewer viewer)
5080: {
5081: PetscBool isbinary, ishdf5;
5083: PetscFunctionBegin;
5086: /* force binary viewer to load .info file if it has not yet done so */
5087: PetscCall(PetscViewerSetUp(viewer));
5088: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERBINARY, &isbinary));
5089: PetscCall(PetscObjectTypeCompare((PetscObject)viewer, PETSCVIEWERHDF5, &ishdf5));
5090: if (isbinary) {
5091: PetscCall(MatLoad_SeqAIJ_Binary(newMat, viewer));
5092: } else if (ishdf5) {
5093: #if defined(PETSC_HAVE_HDF5)
5094: PetscCall(MatLoad_AIJ_HDF5(newMat, viewer));
5095: #else
5096: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "HDF5 not supported in this build.\nPlease reconfigure using --download-hdf5");
5097: #endif
5098: } else {
5099: SETERRQ(PetscObjectComm((PetscObject)newMat), PETSC_ERR_SUP, "Viewer type %s not yet supported for reading %s matrices", ((PetscObject)viewer)->type_name, ((PetscObject)newMat)->type_name);
5100: }
5101: PetscFunctionReturn(PETSC_SUCCESS);
5102: }
5104: PetscErrorCode MatLoad_SeqAIJ_Binary(Mat mat, PetscViewer viewer)
5105: {
5106: Mat_SeqAIJ *a = (Mat_SeqAIJ *)mat->data;
5107: PetscInt header[4], *rowlens, M, N, nz, sum, rows, cols, i;
5109: PetscFunctionBegin;
5110: PetscCall(PetscViewerSetUp(viewer));
5112: /* read in matrix header */
5113: PetscCall(PetscViewerBinaryRead(viewer, header, 4, NULL, PETSC_INT));
5114: PetscCheck(header[0] == MAT_FILE_CLASSID, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Not a matrix object in file");
5115: M = header[1];
5116: N = header[2];
5117: nz = header[3];
5118: PetscCheck(M >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix row size (%" PetscInt_FMT ") in file is negative", M);
5119: PetscCheck(N >= 0, PetscObjectComm((PetscObject)viewer), PETSC_ERR_FILE_UNEXPECTED, "Matrix column size (%" PetscInt_FMT ") in file is negative", N);
5120: PetscCheck(nz >= 0, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix stored in special format on disk, cannot load as SeqAIJ");
5122: /* set block sizes from the viewer's .info file */
5123: PetscCall(MatLoad_Binary_BlockSizes(mat, viewer));
5124: /* set local and global sizes if not set already */
5125: if (mat->rmap->n < 0) mat->rmap->n = M;
5126: if (mat->cmap->n < 0) mat->cmap->n = N;
5127: if (mat->rmap->N < 0) mat->rmap->N = M;
5128: if (mat->cmap->N < 0) mat->cmap->N = N;
5129: PetscCall(PetscLayoutSetUp(mat->rmap));
5130: PetscCall(PetscLayoutSetUp(mat->cmap));
5132: /* check if the matrix sizes are correct */
5133: PetscCall(MatGetSize(mat, &rows, &cols));
5134: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different sizes (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
5136: /* read in row lengths */
5137: PetscCall(PetscMalloc1(M, &rowlens));
5138: PetscCall(PetscViewerBinaryRead(viewer, rowlens, M, NULL, PETSC_INT));
5139: /* check if sum(rowlens) is same as nz */
5140: sum = 0;
5141: for (i = 0; i < M; i++) sum += rowlens[i];
5142: PetscCheck(sum == nz, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Inconsistent matrix data in file: nonzeros = %" PetscInt_FMT ", sum-row-lengths = %" PetscInt_FMT, nz, sum);
5143: /* preallocate and check sizes */
5144: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(mat, 0, rowlens));
5145: PetscCall(MatGetSize(mat, &rows, &cols));
5146: PetscCheck(M == rows && N == cols, PETSC_COMM_SELF, PETSC_ERR_FILE_UNEXPECTED, "Matrix in file of different length (%" PetscInt_FMT ", %" PetscInt_FMT ") than the input matrix (%" PetscInt_FMT ", %" PetscInt_FMT ")", M, N, rows, cols);
5147: /* store row lengths */
5148: PetscCall(PetscArraycpy(a->ilen, rowlens, M));
5149: PetscCall(PetscFree(rowlens));
5151: /* fill in "i" row pointers */
5152: a->i[0] = 0;
5153: for (i = 0; i < M; i++) a->i[i + 1] = a->i[i] + a->ilen[i];
5154: /* read in "j" column indices */
5155: PetscCall(PetscViewerBinaryRead(viewer, a->j, nz, NULL, PETSC_INT));
5156: /* read in "a" nonzero values */
5157: PetscCall(PetscViewerBinaryRead(viewer, a->a, nz, NULL, PETSC_SCALAR));
5159: PetscCall(MatAssemblyBegin(mat, MAT_FINAL_ASSEMBLY));
5160: PetscCall(MatAssemblyEnd(mat, MAT_FINAL_ASSEMBLY));
5161: PetscFunctionReturn(PETSC_SUCCESS);
5162: }
5164: PetscErrorCode MatEqual_SeqAIJ(Mat A, Mat B, PetscBool *flg)
5165: {
5166: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data, *b = (Mat_SeqAIJ *)B->data;
5167: const PetscScalar *aa, *ba;
5168: #if defined(PETSC_USE_COMPLEX)
5169: PetscInt k;
5170: #endif
5172: PetscFunctionBegin;
5173: /* If the matrix dimensions are not equal,or no of nonzeros */
5174: if ((A->rmap->n != B->rmap->n) || (A->cmap->n != B->cmap->n) || (a->nz != b->nz)) {
5175: *flg = PETSC_FALSE;
5176: PetscFunctionReturn(PETSC_SUCCESS);
5177: }
5179: /* if the a->i are the same */
5180: PetscCall(PetscArraycmp(a->i, b->i, A->rmap->n + 1, flg));
5181: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5183: /* if a->j are the same */
5184: PetscCall(PetscArraycmp(a->j, b->j, a->nz, flg));
5185: if (!*flg) PetscFunctionReturn(PETSC_SUCCESS);
5187: PetscCall(MatSeqAIJGetArrayRead(A, &aa));
5188: PetscCall(MatSeqAIJGetArrayRead(B, &ba));
5189: /* if a->a are the same */
5190: #if defined(PETSC_USE_COMPLEX)
5191: for (k = 0; k < a->nz; k++) {
5192: if (PetscRealPart(aa[k]) != PetscRealPart(ba[k]) || PetscImaginaryPart(aa[k]) != PetscImaginaryPart(ba[k])) {
5193: *flg = PETSC_FALSE;
5194: PetscFunctionReturn(PETSC_SUCCESS);
5195: }
5196: }
5197: #else
5198: PetscCall(PetscArraycmp(aa, ba, a->nz, flg));
5199: #endif
5200: PetscCall(MatSeqAIJRestoreArrayRead(A, &aa));
5201: PetscCall(MatSeqAIJRestoreArrayRead(B, &ba));
5202: PetscFunctionReturn(PETSC_SUCCESS);
5203: }
5205: /*@
5206: MatCreateSeqAIJWithArrays - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in CSR format)
5207: provided by the user.
5209: Collective
5211: Input Parameters:
5212: + comm - must be an MPI communicator of size 1
5213: . m - number of rows
5214: . n - number of columns
5215: . i - row indices; that is i[0] = 0, i[row] = i[row-1] + number of elements in that row of the matrix
5216: . j - column indices
5217: - a - matrix values
5219: Output Parameter:
5220: . mat - the matrix
5222: Level: intermediate
5224: Notes:
5225: The `i`, `j`, and `a` arrays are not copied by this routine, the user must free these arrays
5226: once the matrix is destroyed and not before
5228: You cannot set new nonzero locations into this matrix, that will generate an error.
5230: The `i` and `j` indices are 0 based
5232: The format which is used for the sparse matrix input, is equivalent to a
5233: row-major ordering.. i.e for the following matrix, the input data expected is
5234: as shown
5235: .vb
5236: 1 0 0
5237: 2 0 3
5238: 4 5 6
5240: i = {0,1,3,6} [size = nrow+1 = 3+1]
5241: j = {0,0,2,0,1,2} [size = 6]; values must be sorted for each row
5242: v = {1,2,3,4,5,6} [size = 6]
5243: .ve
5245: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateMPIAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`
5246: @*/
5247: PetscErrorCode MatCreateSeqAIJWithArrays(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat)
5248: {
5249: PetscInt ii;
5250: Mat_SeqAIJ *aij;
5251: PetscInt jj;
5253: PetscFunctionBegin;
5254: PetscCheck(m <= 0 || i[0] == 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "i (row indices) must start with 0");
5255: PetscCall(MatCreate(comm, mat));
5256: PetscCall(MatSetSizes(*mat, m, n, m, n));
5257: /* PetscCall(MatSetBlockSizes(*mat,,)); */
5258: PetscCall(MatSetType(*mat, MATSEQAIJ));
5259: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, MAT_SKIP_ALLOCATION, NULL));
5260: aij = (Mat_SeqAIJ *)(*mat)->data;
5261: PetscCall(PetscMalloc1(m, &aij->imax));
5262: PetscCall(PetscMalloc1(m, &aij->ilen));
5264: aij->i = i;
5265: aij->j = j;
5266: aij->a = a;
5267: aij->singlemalloc = PETSC_FALSE;
5268: aij->nonew = -1; /*this indicates that inserting a new value in the matrix that generates a new nonzero is an error*/
5269: aij->free_a = PETSC_FALSE;
5270: aij->free_ij = PETSC_FALSE;
5272: for (ii = 0, aij->nonzerorowcnt = 0, aij->rmax = 0; ii < m; ii++) {
5273: aij->ilen[ii] = aij->imax[ii] = i[ii + 1] - i[ii];
5274: if (PetscDefined(USE_DEBUG)) {
5275: PetscCheck(i[ii + 1] - i[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative row length in i (row indices) row = %" PetscInt_FMT " length = %" PetscInt_FMT, ii, i[ii + 1] - i[ii]);
5276: for (jj = i[ii] + 1; jj < i[ii + 1]; jj++) {
5277: PetscCheck(j[jj] >= j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is not sorted", jj - i[ii], j[jj], ii);
5278: PetscCheck(j[jj] != j[jj - 1], PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column entry number %" PetscInt_FMT " (actual column %" PetscInt_FMT ") in row %" PetscInt_FMT " is identical to previous entry", jj - i[ii], j[jj], ii);
5279: }
5280: }
5281: }
5282: if (PetscDefined(USE_DEBUG)) {
5283: for (ii = 0; ii < aij->i[m]; ii++) {
5284: PetscCheck(j[ii] >= 0, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Negative column index at location = %" PetscInt_FMT " index = %" PetscInt_FMT, ii, j[ii]);
5285: PetscCheck(j[ii] <= n - 1, PETSC_COMM_SELF, PETSC_ERR_ARG_OUTOFRANGE, "Column index to large at location = %" PetscInt_FMT " index = %" PetscInt_FMT " last column = %" PetscInt_FMT, ii, j[ii], n - 1);
5286: }
5287: }
5289: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5290: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5291: PetscFunctionReturn(PETSC_SUCCESS);
5292: }
5294: /*@
5295: MatCreateSeqAIJFromTriple - Creates an sequential `MATSEQAIJ` matrix using matrix elements (in COO format)
5296: provided by the user.
5298: Collective
5300: Input Parameters:
5301: + comm - must be an MPI communicator of size 1
5302: . m - number of rows
5303: . n - number of columns
5304: . i - row indices
5305: . j - column indices
5306: . a - matrix values
5307: . nz - number of nonzeros
5308: - idx - if the `i` and `j` indices start with 1 use `PETSC_TRUE` otherwise use `PETSC_FALSE`
5310: Output Parameter:
5311: . mat - the matrix
5313: Level: intermediate
5315: Example:
5316: For the following matrix, the input data expected is as shown (using 0 based indexing)
5317: .vb
5318: 1 0 0
5319: 2 0 3
5320: 4 5 6
5322: i = {0,1,1,2,2,2}
5323: j = {0,0,2,0,1,2}
5324: v = {1,2,3,4,5,6}
5325: .ve
5327: Note:
5328: Instead of using this function, users should also consider `MatSetPreallocationCOO()` and `MatSetValuesCOO()`, which allow repeated or remote entries,
5329: and are particularly useful in iterative applications.
5331: .seealso: [](ch_matrices), `Mat`, `MatCreate()`, `MatCreateAIJ()`, `MatCreateSeqAIJ()`, `MatCreateSeqAIJWithArrays()`, `MatMPIAIJSetPreallocationCSR()`, `MatSetValuesCOO()`, `MatSetPreallocationCOO()`
5332: @*/
5333: PetscErrorCode MatCreateSeqAIJFromTriple(MPI_Comm comm, PetscInt m, PetscInt n, PetscInt i[], PetscInt j[], PetscScalar a[], Mat *mat, PetscInt nz, PetscBool idx)
5334: {
5335: PetscInt ii, *nnz, one = 1, row, col;
5337: PetscFunctionBegin;
5338: PetscCall(PetscCalloc1(m, &nnz));
5339: for (ii = 0; ii < nz; ii++) nnz[i[ii] - !!idx] += 1;
5340: PetscCall(MatCreate(comm, mat));
5341: PetscCall(MatSetSizes(*mat, m, n, m, n));
5342: PetscCall(MatSetType(*mat, MATSEQAIJ));
5343: PetscCall(MatSeqAIJSetPreallocation_SeqAIJ(*mat, 0, nnz));
5344: for (ii = 0; ii < nz; ii++) {
5345: if (idx) {
5346: row = i[ii] - 1;
5347: col = j[ii] - 1;
5348: } else {
5349: row = i[ii];
5350: col = j[ii];
5351: }
5352: PetscCall(MatSetValues(*mat, one, &row, one, &col, &a[ii], ADD_VALUES));
5353: }
5354: PetscCall(MatAssemblyBegin(*mat, MAT_FINAL_ASSEMBLY));
5355: PetscCall(MatAssemblyEnd(*mat, MAT_FINAL_ASSEMBLY));
5356: PetscCall(PetscFree(nnz));
5357: PetscFunctionReturn(PETSC_SUCCESS);
5358: }
5360: PetscErrorCode MatSeqAIJInvalidateDiagonal(Mat A)
5361: {
5362: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5364: PetscFunctionBegin;
5365: a->idiagvalid = PETSC_FALSE;
5366: a->ibdiagvalid = PETSC_FALSE;
5368: PetscCall(MatSeqAIJInvalidateDiagonal_Inode(A));
5369: PetscFunctionReturn(PETSC_SUCCESS);
5370: }
5372: PetscErrorCode MatCreateMPIMatConcatenateSeqMat_SeqAIJ(MPI_Comm comm, Mat inmat, PetscInt n, MatReuse scall, Mat *outmat)
5373: {
5374: PetscFunctionBegin;
5375: PetscCall(MatCreateMPIMatConcatenateSeqMat_MPIAIJ(comm, inmat, n, scall, outmat));
5376: PetscFunctionReturn(PETSC_SUCCESS);
5377: }
5379: /*
5380: Permute A into C's *local* index space using rowemb,colemb.
5381: The embedding are supposed to be injections and the above implies that the range of rowemb is a subset
5382: of [0,m), colemb is in [0,n).
5383: If pattern == DIFFERENT_NONZERO_PATTERN, C is preallocated according to A.
5384: */
5385: PetscErrorCode MatSetSeqMat_SeqAIJ(Mat C, IS rowemb, IS colemb, MatStructure pattern, Mat B)
5386: {
5387: /* If making this function public, change the error returned in this function away from _PLIB. */
5388: Mat_SeqAIJ *Baij;
5389: PetscBool seqaij;
5390: PetscInt m, n, *nz, i, j, count;
5391: PetscScalar v;
5392: const PetscInt *rowindices, *colindices;
5394: PetscFunctionBegin;
5395: if (!B) PetscFunctionReturn(PETSC_SUCCESS);
5396: /* Check to make sure the target matrix (and embeddings) are compatible with C and each other. */
5397: PetscCall(PetscObjectBaseTypeCompare((PetscObject)B, MATSEQAIJ, &seqaij));
5398: PetscCheck(seqaij, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is of wrong type");
5399: if (rowemb) {
5400: PetscCall(ISGetLocalSize(rowemb, &m));
5401: PetscCheck(m == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Row IS of size %" PetscInt_FMT " is incompatible with matrix row size %" PetscInt_FMT, m, B->rmap->n);
5402: } else {
5403: PetscCheck(C->rmap->n == B->rmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is row-incompatible with the target matrix");
5404: }
5405: if (colemb) {
5406: PetscCall(ISGetLocalSize(colemb, &n));
5407: PetscCheck(n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Diag col IS of size %" PetscInt_FMT " is incompatible with input matrix col size %" PetscInt_FMT, n, B->cmap->n);
5408: } else {
5409: PetscCheck(C->cmap->n == B->cmap->n, PETSC_COMM_SELF, PETSC_ERR_PLIB, "Input matrix is col-incompatible with the target matrix");
5410: }
5412: Baij = (Mat_SeqAIJ *)B->data;
5413: if (pattern == DIFFERENT_NONZERO_PATTERN) {
5414: PetscCall(PetscMalloc1(B->rmap->n, &nz));
5415: for (i = 0; i < B->rmap->n; i++) nz[i] = Baij->i[i + 1] - Baij->i[i];
5416: PetscCall(MatSeqAIJSetPreallocation(C, 0, nz));
5417: PetscCall(PetscFree(nz));
5418: }
5419: if (pattern == SUBSET_NONZERO_PATTERN) PetscCall(MatZeroEntries(C));
5420: count = 0;
5421: rowindices = NULL;
5422: colindices = NULL;
5423: if (rowemb) PetscCall(ISGetIndices(rowemb, &rowindices));
5424: if (colemb) PetscCall(ISGetIndices(colemb, &colindices));
5425: for (i = 0; i < B->rmap->n; i++) {
5426: PetscInt row;
5427: row = i;
5428: if (rowindices) row = rowindices[i];
5429: for (j = Baij->i[i]; j < Baij->i[i + 1]; j++) {
5430: PetscInt col;
5431: col = Baij->j[count];
5432: if (colindices) col = colindices[col];
5433: v = Baij->a[count];
5434: PetscCall(MatSetValues(C, 1, &row, 1, &col, &v, INSERT_VALUES));
5435: ++count;
5436: }
5437: }
5438: /* FIXME: set C's nonzerostate correctly. */
5439: /* Assembly for C is necessary. */
5440: C->preallocated = PETSC_TRUE;
5441: C->assembled = PETSC_TRUE;
5442: C->was_assembled = PETSC_FALSE;
5443: PetscFunctionReturn(PETSC_SUCCESS);
5444: }
5446: PetscErrorCode MatEliminateZeros_SeqAIJ(Mat A, PetscBool keep)
5447: {
5448: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5449: MatScalar *aa = a->a;
5450: PetscInt m = A->rmap->n, fshift = 0, fshift_prev = 0, i, k;
5451: PetscInt *ailen = a->ilen, *imax = a->imax, *ai = a->i, *aj = a->j, rmax = 0;
5453: PetscFunctionBegin;
5454: PetscCheck(A->assembled, PETSC_COMM_SELF, PETSC_ERR_ARG_WRONGSTATE, "Cannot eliminate zeros for unassembled matrix");
5455: if (m) rmax = ailen[0]; /* determine row with most nonzeros */
5456: for (i = 1; i <= m; i++) {
5457: /* move each nonzero entry back by the amount of zero slots (fshift) before it*/
5458: for (k = ai[i - 1]; k < ai[i]; k++) {
5459: if (aa[k] == 0 && (aj[k] != i - 1 || !keep)) fshift++;
5460: else {
5461: if (aa[k] == 0 && aj[k] == i - 1) PetscCall(PetscInfo(A, "Keep the diagonal zero at row %" PetscInt_FMT "\n", i - 1));
5462: aa[k - fshift] = aa[k];
5463: aj[k - fshift] = aj[k];
5464: }
5465: }
5466: ai[i - 1] -= fshift_prev; // safe to update ai[i-1] now since it will not be used in the next iteration
5467: fshift_prev = fshift;
5468: /* reset ilen and imax for each row */
5469: ailen[i - 1] = imax[i - 1] = ai[i] - fshift - ai[i - 1];
5470: a->nonzerorowcnt += ((ai[i] - fshift - ai[i - 1]) > 0);
5471: rmax = PetscMax(rmax, ailen[i - 1]);
5472: }
5473: if (fshift) {
5474: if (m) {
5475: ai[m] -= fshift;
5476: a->nz = ai[m];
5477: }
5478: PetscCall(PetscInfo(A, "Matrix size: %" PetscInt_FMT " X %" PetscInt_FMT "; zeros eliminated: %" PetscInt_FMT "; nonzeros left: %" PetscInt_FMT "\n", m, A->cmap->n, fshift, a->nz));
5479: A->nonzerostate++;
5480: A->info.nz_unneeded += (PetscReal)fshift;
5481: a->rmax = rmax;
5482: if (a->inode.use && a->inode.checked) PetscCall(MatSeqAIJCheckInode(A));
5483: PetscCall(MatAssemblyBegin(A, MAT_FINAL_ASSEMBLY));
5484: PetscCall(MatAssemblyEnd(A, MAT_FINAL_ASSEMBLY));
5485: }
5486: PetscFunctionReturn(PETSC_SUCCESS);
5487: }
5489: PetscFunctionList MatSeqAIJList = NULL;
5491: /*@C
5492: MatSeqAIJSetType - Converts a `MATSEQAIJ` matrix to a subtype
5494: Collective
5496: Input Parameters:
5497: + mat - the matrix object
5498: - matype - matrix type
5500: Options Database Key:
5501: . -mat_seqaij_type <method> - for example seqaijcrl
5503: Level: intermediate
5505: .seealso: [](ch_matrices), `Mat`, `PCSetType()`, `VecSetType()`, `MatCreate()`, `MatType`
5506: @*/
5507: PetscErrorCode MatSeqAIJSetType(Mat mat, MatType matype)
5508: {
5509: PetscBool sametype;
5510: PetscErrorCode (*r)(Mat, MatType, MatReuse, Mat *);
5512: PetscFunctionBegin;
5514: PetscCall(PetscObjectTypeCompare((PetscObject)mat, matype, &sametype));
5515: if (sametype) PetscFunctionReturn(PETSC_SUCCESS);
5517: PetscCall(PetscFunctionListFind(MatSeqAIJList, matype, &r));
5518: PetscCheck(r, PetscObjectComm((PetscObject)mat), PETSC_ERR_ARG_UNKNOWN_TYPE, "Unknown Mat type given: %s", matype);
5519: PetscCall((*r)(mat, matype, MAT_INPLACE_MATRIX, &mat));
5520: PetscFunctionReturn(PETSC_SUCCESS);
5521: }
5523: /*@C
5524: MatSeqAIJRegister - - Adds a new sub-matrix type for sequential `MATSEQAIJ` matrices
5526: Not Collective
5528: Input Parameters:
5529: + sname - name of a new user-defined matrix type, for example `MATSEQAIJCRL`
5530: - function - routine to convert to subtype
5532: Level: advanced
5534: Notes:
5535: `MatSeqAIJRegister()` may be called multiple times to add several user-defined solvers.
5537: Then, your matrix can be chosen with the procedural interface at runtime via the option
5538: $ -mat_seqaij_type my_mat
5540: .seealso: [](ch_matrices), `Mat`, `MatSeqAIJRegisterAll()`
5541: @*/
5542: PetscErrorCode MatSeqAIJRegister(const char sname[], PetscErrorCode (*function)(Mat, MatType, MatReuse, Mat *))
5543: {
5544: PetscFunctionBegin;
5545: PetscCall(MatInitializePackage());
5546: PetscCall(PetscFunctionListAdd(&MatSeqAIJList, sname, function));
5547: PetscFunctionReturn(PETSC_SUCCESS);
5548: }
5550: PetscBool MatSeqAIJRegisterAllCalled = PETSC_FALSE;
5552: /*@C
5553: MatSeqAIJRegisterAll - Registers all of the matrix subtypes of `MATSSEQAIJ`
5555: Not Collective
5557: Level: advanced
5559: Note:
5560: This registers the versions of `MATSEQAIJ` for GPUs
5562: .seealso: [](ch_matrices), `Mat`, `MatRegisterAll()`, `MatSeqAIJRegister()`
5563: @*/
5564: PetscErrorCode MatSeqAIJRegisterAll(void)
5565: {
5566: PetscFunctionBegin;
5567: if (MatSeqAIJRegisterAllCalled) PetscFunctionReturn(PETSC_SUCCESS);
5568: MatSeqAIJRegisterAllCalled = PETSC_TRUE;
5570: PetscCall(MatSeqAIJRegister(MATSEQAIJCRL, MatConvert_SeqAIJ_SeqAIJCRL));
5571: PetscCall(MatSeqAIJRegister(MATSEQAIJPERM, MatConvert_SeqAIJ_SeqAIJPERM));
5572: PetscCall(MatSeqAIJRegister(MATSEQAIJSELL, MatConvert_SeqAIJ_SeqAIJSELL));
5573: #if defined(PETSC_HAVE_MKL_SPARSE)
5574: PetscCall(MatSeqAIJRegister(MATSEQAIJMKL, MatConvert_SeqAIJ_SeqAIJMKL));
5575: #endif
5576: #if defined(PETSC_HAVE_CUDA)
5577: PetscCall(MatSeqAIJRegister(MATSEQAIJCUSPARSE, MatConvert_SeqAIJ_SeqAIJCUSPARSE));
5578: #endif
5579: #if defined(PETSC_HAVE_HIP)
5580: PetscCall(MatSeqAIJRegister(MATSEQAIJHIPSPARSE, MatConvert_SeqAIJ_SeqAIJHIPSPARSE));
5581: #endif
5582: #if defined(PETSC_HAVE_KOKKOS_KERNELS)
5583: PetscCall(MatSeqAIJRegister(MATSEQAIJKOKKOS, MatConvert_SeqAIJ_SeqAIJKokkos));
5584: #endif
5585: #if defined(PETSC_HAVE_VIENNACL) && defined(PETSC_HAVE_VIENNACL_NO_CUDA)
5586: PetscCall(MatSeqAIJRegister(MATMPIAIJVIENNACL, MatConvert_SeqAIJ_SeqAIJViennaCL));
5587: #endif
5588: PetscFunctionReturn(PETSC_SUCCESS);
5589: }
5591: /*
5592: Special version for direct calls from Fortran
5593: */
5594: #include <petsc/private/fortranimpl.h>
5595: #if defined(PETSC_HAVE_FORTRAN_CAPS)
5596: #define matsetvaluesseqaij_ MATSETVALUESSEQAIJ
5597: #elif !defined(PETSC_HAVE_FORTRAN_UNDERSCORE)
5598: #define matsetvaluesseqaij_ matsetvaluesseqaij
5599: #endif
5601: /* Change these macros so can be used in void function */
5603: /* Change these macros so can be used in void function */
5604: /* Identical to PetscCallVoid, except it assigns to *_ierr */
5605: #undef PetscCall
5606: #define PetscCall(...) \
5607: do { \
5608: PetscErrorCode ierr_msv_mpiaij = __VA_ARGS__; \
5609: if (PetscUnlikely(ierr_msv_mpiaij)) { \
5610: *_ierr = PetscError(PETSC_COMM_SELF, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr_msv_mpiaij, PETSC_ERROR_REPEAT, " "); \
5611: return; \
5612: } \
5613: } while (0)
5615: #undef SETERRQ
5616: #define SETERRQ(comm, ierr, ...) \
5617: do { \
5618: *_ierr = PetscError(comm, __LINE__, PETSC_FUNCTION_NAME, __FILE__, ierr, PETSC_ERROR_INITIAL, __VA_ARGS__); \
5619: return; \
5620: } while (0)
5622: PETSC_EXTERN void matsetvaluesseqaij_(Mat *AA, PetscInt *mm, const PetscInt im[], PetscInt *nn, const PetscInt in[], const PetscScalar v[], InsertMode *isis, PetscErrorCode *_ierr)
5623: {
5624: Mat A = *AA;
5625: PetscInt m = *mm, n = *nn;
5626: InsertMode is = *isis;
5627: Mat_SeqAIJ *a = (Mat_SeqAIJ *)A->data;
5628: PetscInt *rp, k, low, high, t, ii, row, nrow, i, col, l, rmax, N;
5629: PetscInt *imax, *ai, *ailen;
5630: PetscInt *aj, nonew = a->nonew, lastcol = -1;
5631: MatScalar *ap, value, *aa;
5632: PetscBool ignorezeroentries = a->ignorezeroentries;
5633: PetscBool roworiented = a->roworiented;
5635: PetscFunctionBegin;
5636: MatCheckPreallocated(A, 1);
5637: imax = a->imax;
5638: ai = a->i;
5639: ailen = a->ilen;
5640: aj = a->j;
5641: aa = a->a;
5643: for (k = 0; k < m; k++) { /* loop over added rows */
5644: row = im[k];
5645: if (row < 0) continue;
5646: PetscCheck(row < A->rmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Row too large");
5647: rp = aj + ai[row];
5648: ap = aa + ai[row];
5649: rmax = imax[row];
5650: nrow = ailen[row];
5651: low = 0;
5652: high = nrow;
5653: for (l = 0; l < n; l++) { /* loop over added columns */
5654: if (in[l] < 0) continue;
5655: PetscCheck(in[l] < A->cmap->n, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Column too large");
5656: col = in[l];
5657: if (roworiented) value = v[l + k * n];
5658: else value = v[k + l * m];
5660: if (value == 0.0 && ignorezeroentries && (is == ADD_VALUES)) continue;
5662: if (col <= lastcol) low = 0;
5663: else high = nrow;
5664: lastcol = col;
5665: while (high - low > 5) {
5666: t = (low + high) / 2;
5667: if (rp[t] > col) high = t;
5668: else low = t;
5669: }
5670: for (i = low; i < high; i++) {
5671: if (rp[i] > col) break;
5672: if (rp[i] == col) {
5673: if (is == ADD_VALUES) ap[i] += value;
5674: else ap[i] = value;
5675: goto noinsert;
5676: }
5677: }
5678: if (value == 0.0 && ignorezeroentries) goto noinsert;
5679: if (nonew == 1) goto noinsert;
5680: PetscCheck(nonew != -1, PetscObjectComm((PetscObject)A), PETSC_ERR_ARG_OUTOFRANGE, "Inserting a new nonzero in the matrix");
5681: MatSeqXAIJReallocateAIJ(A, A->rmap->n, 1, nrow, row, col, rmax, aa, ai, aj, rp, ap, imax, nonew, MatScalar);
5682: N = nrow++ - 1;
5683: a->nz++;
5684: high++;
5685: /* shift up all the later entries in this row */
5686: for (ii = N; ii >= i; ii--) {
5687: rp[ii + 1] = rp[ii];
5688: ap[ii + 1] = ap[ii];
5689: }
5690: rp[i] = col;
5691: ap[i] = value;
5692: A->nonzerostate++;
5693: noinsert:;
5694: low = i + 1;
5695: }
5696: ailen[row] = nrow;
5697: }
5698: PetscFunctionReturnVoid();
5699: }
5700: /* Undefining these here since they were redefined from their original definition above! No
5701: * other PETSc functions should be defined past this point, as it is impossible to recover the
5702: * original definitions */
5703: #undef PetscCall
5704: #undef SETERRQ